US20070203589A1

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Publication number: US20070203589A1
Application number: US11/559,145
Authority: US
Inventors: Steven Flinn; Naomi Moneypenny
Original assignee: ManyWorlds Inc
Current assignee: ManyWorlds Inc
Priority date: 2005-04-08
Filing date: 2006-11-13
Publication date: 2007-08-30
Also published as: US20180232671A1; US20120030030A1; US20120030063A1; US20180225598A1; US20120035976A1; US10783464B2; US20120035970A1; US20120030029A1; US20180232669A1; US20190325359A1; US20120035969A1; US20180232670A1; US20180314992A1; US20120089915A1; US20120036020A1; US20120030621A1; US20180225599A1; US20120030028A1; US20120030064A1; US20120030620A1

Abstract

Adaptive recombinant processes enable business processes to be more adaptive to the changing needs and preferences of participants in the processes, and other business circumstances. Processes such as marketing, business development, and innovation can adapt to the needs of process participants through application of preference inferences derived from systems usage behaviors associated with process participants. Application of adaptive recombinant systems can therefore enable a process to become more valuable to the participants in the process through the facilitation of direct or indirect collaborative learning.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims priority under 35 U.S.C. § 119 to PCT Patent Application No. PCT/US2005/011951, which claimed priority under 35 U.S.C. § 119(e) to U.S. Provisional Patent Application Ser. No. 60/572,565, entitled “A Method and System for Adaptive Fuzzy Processes,” filed May 20, 2004.

FIELD OF THE INVENTION

This invention relates to extending the business process paradigm so as to make processes more explicitly adaptive over time. More specifically, adaptive recombinant processes relates to processes that automatically structure and re-structure themselves so as to deliver increasing value to the participants in the processes over time.

BACKGROUND OF THE INVENTION

The business process paradigm was first introduced in a rigorous form by Rummler and Brache in the late 1980's, and was increasingly popularized by authors such as Michael Hammer, and a wide range of business consultants, during the 1990's. The terms “process redesign” or “process reengineering” have been typically used to denote the explicit establishment of processes that are optimized for specific business requirements. It should be understood that although the modifier “business” may be applied to the term “process” herein, processes are relevant to, and may apply to, non-business organizations or institutions, as well as individuals.

Business processes can be broadly defined as a set of activities that collectively perform a business function. The activities within a process are typically performed in a specific sequence, with the sequence of activities subsequent to any specified activity being potentially dependent on conditions and decisions taken at the previous activity step.

The prior art associated with process design constitutes developing processes that are optimized for current business conditions, while attempting to build in enough flexibility in the design of the process for the process to remain effective if business conditions change within a limited range over time. Training of individuals performing tasks within processes is often a mixture of formalized training, classroom and/or on-line training, as well as on-the-job experience. In general, however, the current process paradigm is not one of adaptive processes; that is, processes that can effectively change as business conditions change without significant, explicit human redesign efforts, and processes that adapt to the on-going learning needs, and more generally, the preferences or interests, of individual participants in the processes. Specifically, the current process paradigm does not have a built-in learning mechanism, resulting in a significant penalty in efficiency and effectiveness.

SUMMARY OF THE INVENTION

In accordance with the embodiments described herein, a method and system for adaptive recombinant processes is disclosed.

The present invention, “adaptive recombinant processes,” is a method and system for embedding adaptation and learning within any type of process. Adaptive recombinant processes enable design and implementation of processes that automatically capture process participant behaviors associated with the use of, interaction with, or, most generally, participation in, the associated process. These process participant behaviors include both individual and community usage behaviors. The resulting adaptive process can thereby effectively reconfigure itself on a continuous and potentially real-time basis, based, at least in part, on inferences of preferences or interests derived from process interactions by participants in the process. Such inferences may be conducted on an automatic or semi-automatic basis; in either case, application of the inferences can potentially dramatically reduce explicit, manual process design and redesign efforts. Adaptive recombinant processes can also dramatically reduce traditional training costs, and effectively integrates the domains of e-learning and knowledge management directly within business processes.

Furthermore, adaptive recombinant processes can enable the syndication of processes or elements of processes among organizations, which can then be automatically or semi-automatically integrated with existing processes or process elements. This recombinant process approach can significantly increase process adaptiveness and increase efficiency through the maximizing of reuse. Furthermore, an evolutionary approach may be used to create a diversity of processes that can be evaluated automatically or semi-automatically, and then preferentially combined based on evaluation results.

Adaptive recombinant processes enables both increasing the adaptiveness of existing classes of processes and the enablement of entirely new types of processes that were not feasible with prior methods. An example of increasing the adaptiveness of existing processes is building in “real-time learning” within any instance of existing classes of processes, to create an adaptive “cockpit” that facilitates process learning, use and execution. Examples of new types of processes enabled by adaptive recombinant processes include processes that are underpinned by syndication and/or recombination of processes and sub-processes across a series or network of organizations. Such capabilities may be applied to facilitate, for example, marketing and business development, product or service/solution development and delivery, innovation, coordinated operations, and/or collaborative learning. Specific examples of new types of processes enabled by adaptive recombinant processes are adaptive online asset management, adaptive viral marketing processes, adaptive sales and marketing processes, adaptive commercial processes such as adaptive product and service bundling and pricing, processes enabled by location-aware and collectively adaptive systems, and adaptive publishing processes.

Adaptive recombinant processes can apply the fuzzy content network approach as defined in U.S. Pat. No. 6,795,826, entitled “Fuzzy Content Network Management and Access,” and adaptive recombinant systems approaches as defined in PCT Patent Application No. PCT/US04/37176, entitled “Adaptive Recombinant Systems,” filed on Nov. 4, 2004, both of which are incorporated by reference herein, as if set forth in their entirety.

Other features and embodiments will become apparent from the following description, from the drawings, and from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A and 1B are block diagrams of process and organization topologies, according to the prior art;

FIGS. 2A and 2B are block diagrams of sub-processes and activities, according to the prior art;

FIG. 3 is a block diagram describing the relationship between a process and associated supporting content and computer applications, according to the prior art;

FIG. 4A is a block diagram of an adaptive process, according to some embodiments;

FIG. 4B is a detailed block diagram of the adaptive process ofFIG. 4A, according to some embodiments;

FIG. 4C is a block diagram of an adaptive recombinant process, according to some embodiments;

FIG. 5 is a diagram of the process participant usage framework, according to some embodiments;

FIG. 6 is a diagram of process participant communities and associated relationships, according to some embodiments;

FIG. 7 is a block diagram of an adaptive system, according to some embodiments;

FIG. 8 is a block diagram contrasting the adaptive system ofFIG. 7 with a non-adaptive system, according to some embodiments;

FIG. 9A is a block diagram of the structural aspect of the adaptive system ofFIG. 7, according to some embodiments;

FIG. 9B is a block diagram of the content aspect of the adaptive system ofFIG. 7, according to some embodiments;

FIG. 9C is a block diagram of the usage aspect of the adaptive system ofFIG. 7, according to some embodiments;

FIG. 10 is a block diagram of the adaptive recommendations function used by the adaptive system ofFIG. 7, according to some embodiments;

FIG. 11 is a block diagram showing structural subsets generated by the adaptive recommendations function ofFIG. 7, according to some embodiments;

FIG. 12 is a flow chart showing how recommendations of the adaptive system ofFIG. 7 are generated, whether to support system navigation and use or to update structural or content aspects of the adaptive system, according to some embodiments;

FIG. 13 is a block diagram of a fuzzy network selection operation, according to some embodiments;

FIG. 14 is a block diagram of the adaptive system ofFIG. 7 in which the structural aspect is a fuzzy network, according to some embodiments;

FIG. 15 is a block diagram of a structural aspect including multiple network-based structures, according to some embodiments;

FIG. 16 is a block diagram of an adaptive recombinant system, according to some embodiments;

FIG. 17 is a block diagram of the adaptive recombinant system ofFIG. 16 in which the structural aspect is a fuzzy network, according to some embodiments;

FIG. 18 is a block diagram of the fuzzy network operators used by the adaptive recombinant system ofFIG. 16, according to some embodiments;

FIGS. 19A and 19B are block diagrams of alternative topologies between fuzzy networks and adaptive processes, according to some embodiments;

FIGS. 20A and 20B are block diagrams of a process topic object and a process content object, respectively, according to some embodiments;

FIGS. 21A and 21B are block diagrams of alternative structures of process activity objects, according to some embodiments;

FIGS. 22A and 22B are block diagrams of process activity networks, according to some embodiments;

FIGS. 23A and 23B are block diagrams of a process network, according to some embodiments;

FIG. 24 is a flow diagram describing structural modification of the process network ofFIGS. 23A and 23B, according to some embodiments;

FIG. 25 is a block diagram of a process network selection operation, according to some embodiments;

FIG. 26 is a block diagram of a process network syndication operation, according to some embodiments;

FIG. 27 is a block diagram of a process network resulting from a combination of process networks, according to some embodiments;

FIG. 28 is a block diagram of the adaptive system ofFIG. 7 in which the structural aspect is a process network, according to some embodiments;

FIG. 29 is a block diagram of the adaptive recombinant system ofFIG. 16 in which the structural aspect is a process network, according to some embodiments;

FIGS. 30A and 30B are block diagrams illustrating syndication and recombination of process networks and process network subsets, according to some embodiments;

FIGS. 31A and 31B are block diagrams illustrating syndication and recursive recombination of process networks and process network subsets, according to some embodiments;

FIG. 32 is a block diagram of the process network topologies, according to some embodiments;

FIG. 33 is a block diagram of extensions to the process network topologies ofFIG. 32, according to some embodiments;

FIG. 34 is a diagram of a process lifecycle framework, according to some embodiments;

FIG. 35 is a diagram of process functionality layers, according to some embodiments;

FIG. 36 is a diagram of a process lifecycle management framework, according to some embodiments;

FIG. 37 is a block diagram of an adaptive asset management system and process, according to some embodiments;

FIG. 38 is a block diagram of a real-time learning system interface, according to some embodiments;

FIG. 39 is a block diagram of an adaptive system to support an innovation process, according to some embodiments;

FIG. 40 is a block diagram of a system and process for adaptive publishing, according to some embodiments;

FIG. 41 is a block diagram of a system and process for adaptive commerce, according to some embodiments;

FIG. 42 is a block diagram of a system and process for adaptive price discovery, according to some embodiments;

FIG. 43 is a block diagram of a system and process for adaptive commercial solutions, according to some embodiments;

FIG. 44 is a block diagram of location aware collectively adaptive systems, according to some embodiments;

FIG. 45 is a block diagram of a possible configuration of the location aware collectively adaptive systems ofFIG. 44, according to some embodiments;

FIG. 46 is a block diagram of an alternative configuration of the location aware collectively adaptive systems ofFIG. 45, according to some embodiments;

FIG. 47 is a block diagram of syndication and combination of content networks within the structural aspect of the adaptive recombinant system ofFIG. 16, according to some embodiments;

FIG. 48 is a block diagram of syndication and combination of elements of the structural aspects and usage aspects across multiple instances of adaptive systems ofFIG. 7 within the adaptive recombinant system ofFIG. 16, according to some embodiments;

FIGS. 49A and 49B are block diagrams of recursive syndication and combination of networks of the structural aspects of the adaptive recombinant systems ofFIG. 47 or48 across organizations, according to some embodiments;

FIG. 50 is a block diagram of an evolvable adaptive recombinant system and process, according to some embodiments; and

FIG. 51 is a diagram of alternative computing topologies of adaptive recombinant processes, according to some embodiments.

DETAILED DESCRIPTION

In the following description, numerous details are set forth to provide an understanding of the present invention. However, it will be understood by those skilled in the art that the present invention may be practiced without these details and that numerous variations or modifications from the described embodiments may be possible.

In accordance with the embodiments described herein, a method and a system for development, management and application of adaptive processes is disclosed.

Processes

Processes are ubiquitous throughout the business world, and apply as well to non-business institutions such as government and non-profit organizations and institutions. In the following descriptions of processes and the application of adaptive recombinant processes, business examples will typically be used, but it should be understood that the descriptions of processes and related features, and the application of adaptive recombinant processes, extends to non-business institutions and organizations.

Processes can be defined as categorizations of activities, along with associated inputs and outputs. A process may apply to, but is not limited to, the following general application areas: marketing, sales, price determination, innovation, research and development (R&D), product development, service and solutions development, business development, tangible or intangible asset management, manufacturing, supply chain management, logistics and transportation, procurement, finance and accounting, investment and portfolio management, human resources, education, entertainment, information technology, security, military, legal, administrative processes and business strategy.

FIGS. 1A, 1B,2A,2B and3 describe prior art and definitions associated with processes.

FIG. 1A depicts abusiness enterprise110 including a plurality of processes, a specific example being “process3”105. A business may include one or more processes. It is a typical practice to determine a number of processes that can be effectively remembered and managed by people in the associated business—for example, seven processes (plus or minus two) is a commonly selected number of processes for an organization. Although not explicitly shown inFIG. 1A, each process may have one or more linkages to another process. The linkages may denote a workflow between the processes, or the linkage may denote an information flow, or a linkage may denote both workflow and information flow.

As depicted inFIG. 1B, processes may extend across businesses or enterprises, or most broadly, organizations. For example, inFIG. 1B, “Process8”120 is shown extending across “Enterprise A”110A and “Enterprise B”110B. It should be understood that, in general, multiple processes may extend across multiple enterprises or organizations.

FIG. 2A illustrates that eachprocess125 may include one or more sub-processes. As in the case of processes, sub-processes may have one or more directedlinkages132 to other sub-processes within the process, or to processes outside the process within which the sub-process exists. These external links may constituteinbound links132aoroutbound links132d. There may exist a plurality of links between any two sub-processes, and the plurality of links may include inbound132boroutbound links132c. Although not explicitly shown inFIG. 2A, each sub-process may contain one or more other sub-processes, and this recursive decomposition of sub-processes can continue without limit. It should be noted, as defined herein, that the only essential distinguishing feature of a sub-process with regard to a process is that a sub-process is understood to be a subset of a process. Where the term sub-process is used herein, it is understood that the term process could be used without loss of generality.

FIG. 2B depicts a sub-process. A sub-process135 is comprised of other sub-processes (not shown), and/or a series of activities, for example, “Activity1”140. These activities are conducted byprocess participants200. In a business setting, each activity typically represents a unit of work to be conducted in a prescribed manner by one ormore participants200 in the process, and possibly according to a prescribed workflow. However, as defined herein, an activity may also simply constitute aprocess participant200 action or behavior. For example, aprocess participant200 for a sales process might be a prospective customer, and a behavior of the prospective customer may constitute an activity. In such cases a process participant, for example, a customer or prospective customer, may not be aware that their behaviors or interactions with a process constitute conducting a formally defined activity, although from the perspective of another process participant or the process owner, the activity may constitute a formally defined activity.

Participants in aprocess200, or “process participants,” are defined as individuals that perform some activity within a process, or otherwise interact with a process, or provide input to, or use the output from, a process or sub-process. For example, a process participant in a sales process may include sales people that perform selling activities, but may also include customers or prospective customers that interact with the sales process, including the review and consideration of, and/or the purchasing of goods or services. Further, managers who rely on input from, and/or provide guidance to, the sales process may be considered process participants in the sales process. Further, specific actions or behaviors of the customer or prospective customer may be defined as activities corresponding to the process or sub-process.

Although more than one activity is depicted inFIG. 2B, it should be understood that a process or sub-process may include only a single activity.

Any two activities may be linked, which implies a temporal sequencing or workflow, as for example thelinkage155 between “Activity1”140 and “Activity2”150. An activity may be cross-linked, back linked, or forward linked to more than one other activity. An activity may contain conditional decisions that determine which forward links to other activities, such as depicted by

links

155aand155b, are selected during execution of theantecedent activity150. Parallel activities may exist as represented by “Activity3”161 and “Activity4”160.Inbound links145 to activities of the sub-process135 from other processes, sub-processes or activities may exist, as well asoutbound links165 from activities of the sub-process135 to other processes, sub-processes, or activities.

FIG. 3 illustrates a general approach to information and computing infrastructure support for processes. The workflow of activities within a process or sub-process168 may be managed by a computer-basedworkflow application169 that enables the appropriate sequencing of workflow. Each activity, as for example “Activity2”170, may be supported by on-line content orcomputer applications175. On-line content orcomputer applications175 includepure content180, acomputer application181, and a computer application that includescontent182. Information or content may be accessed by the sub-process168 from each of these sources, shown ascontent access180a,information access181a, andinformation access182a.

For example,content180 may be accessed180a(acontent access180a) as anactivity170 is executed. Although multiple activities are depicted inFIG. 3, a process or sub-process may include only one activity. The term “content” is defined broadly herein, to include text, graphics, video, audio, multi-media, computer programs or any other means of conveying relevant information. During execution of theactivity170, aninteractive computer application181 may be accessed. During execution of theactivity170,information181amay be delivered to, as well as received from thecomputer application181. Acomputer application182, accessible byprocess participants200 during execution of theactivity170, and providing and receivinginformation182aduring execution of theactivity170, may also contain and manage content such that content and computer applications and functions that support anactivity170 may be combined within acomputer application182. An unlimited number of content and computer applications may support a given activity, sub-process or process. Acomputer application182 may directly contain the functionality to manageworkflow169 for the sub-process168, or the workflow functionality may be provided by a separate computer-based application.

Adaptive Processes

FIGS. 4A and 4B depict the application of adaptive recommendations to support a process or sub-process, according to some embodiments. InFIG. 4A, anadaptive process900 is depicted, which includes one ormore process participants200, an adaptive instance of a process or sub-process930 (hereinafter,adaptive process instance930 or process instance930), and an adaptive computer-basedapplication925. InFIG. 4B, theadaptive process900 may include many of the features of the prior art process inFIG. 3. Thus, theadaptive process instance930 features theworkflow application169, if applicable, withmultiple activities170, one or more of which may be linked. Further, the adaptive computer-basedapplication925 is depicted as part of supporting content andcomputer applications175.FIG. 4A provides a broad overview of theadaptive process900 whileFIG. 4B includes many more details.

One ormore participants200 in theadaptive process instance930 generate behaviors associated with their participation in theprocess instance930. The participation in theprocess instance930 may include interactions with computer-basedsystems181 andcontent180, such ascontent access180aandinformation access181a, but may also include behaviors not directly associated with interactions with computer-based systems or content.

Process participants

200 may be identified by the adaptive computer-basedapplication925 through any means of computer-based identification, including, but not limited to, sign-in protocols or bio-metric-based means of identification; or through indirect means based on identification inferences derived from selectiveprocess usage behaviors920.

Theadaptive process900 includes an adaptive computer-basedapplication925, which includes one or more system elements or objects, each element or object being executable software and/or content that is meant for direct human access. The adaptive computer-basedapplication925 tracks and stores selectiveprocess participant behaviors920 associated with aprocess instance930. It should be understood that the tracking and storing of selective behaviors by the adaptive computer-basedapplication925 may also be associated with one or more other processes, sub-processes, and activities other than theprocess instance930, though this is not explicitly depicted inFIGS. 4A and 4B. In addition to the direct tracking and storing of selective process usage behaviors, the adaptive computer-basedapplication925 may also indirectly acquire selective behaviors associated with process usage through one or more other computer-based applications that track and store selective process participant behaviors.

FIGS. 4A and 4B also depictadaptive recommendations910 being generated and delivered by the adaptive computer-basedapplication925 to processparticipants200. Theadaptive recommendations910 are shown being delivered to one ormore process participants200 engaged in “Activity2”170 of theadaptive process instance930 inFIG. 4B. It should be understood that theadaptive recommendations910 may be delivered to processparticipants200 during any activity or any other point during participation in a process or sub-process.

Theadaptive recommendations910 delivered by the adaptive computer-basedapplication925 are informational or computing elements or subsets of the adaptive computer-basedapplication925, and may take the form of text, graphics, Web sites, audio, video, interactive content, other computer applications, or embody any other type or item of information. These recommendations are generated to facilitate participation in, or use of, an associated process, sub-process, or activity. The recommendations are derived by combining the context of what the process participant is currently doing and the inferred preferences or interests of the process participant based, at least in part, on the behaviors of one or more process participants, to generate recommendations. As the process, sub-process or activity is executed more often by the one or more process participants, the recommendations adapt to become increasingly effective. Hence, theadaptive process900 itself can adapt over time to become increasingly effective.

Furthermore, theadaptive recommendations910 may be applied to automatically or semi-automatically self-modify905 the structure, elements, objects, content, information, or software of asubset1632 of the adaptive computer-basedapplication925, including representations of process workflow. (The terms “semi-automatic” or “semi-automatically,” as used herein, are defined to mean that the described activity is conducted through a combination of one or more automatic computer-based operations and one or more direct human interventions.) For example, the elements, objects, or items of content of the adaptive computer-basedapplication925, or the relationships among elements, objects, or items of content associated with the adaptive computer-basedapplication925 may be modified905 based on inferred preferences or interests of one or more process participants. These modifications may be based solely on inferred preferences or interests of the one ormore process participants200 derived from process usage behaviors, or the modifications may be based on inferences of preferences or interests ofprocess participants200 from process usage behaviors integrated with inferences based on the intrinsic characteristics of elements, objects or items of content of the adaptive computer-basedapplication925. These intrinsic characteristics may include patterns of text, images, audio, or any other information-based patterns.

For example, inferences of subject matter based on the statistical patterns of words or phrases in a text-based item of content associated with the adaptive computer-basedapplication925 may be integrated with inferences derived from the process usage behaviors of one or more process participants to generateadaptive recommendations910 that may be applied to deliver to participants in the process, or may be applied to modify905 the structure of the adaptive computer-basedapplication925, including the elements, objects, or items of content of the adaptive computer-basedapplication925, or the relationships among elements, objects, or items of content associated with the adaptive computer-basedapplication925.

Structural modifications

905 applied to the adaptive computer-basedapplication925 enables the structure to adapt to process participant preferences, interests, or requirements over time by embedding inferences on these preferences, interests or requirements directly within the structure of the adaptive computer-basedapplication925 on a persistent basis.

Adaptive recommendations generated by the adaptive computer-basedapplication925 may be applied to modify the structure, including objects and items of content, of other computer-basedsystems175, including the computer-basedworkflow application169, supporting, or accessible by, participants in theprocess instance930. For example, a system that managesworkflow169 may be modified through application of adaptive recommendations generated by the adaptive computer-basedapplication925, potentially altering activity sequencing or other workflow aspects for one or more process participants associated with theadaptive process instance930.

In addition toadaptive recommendations910 being delivered to processparticipants200,process participants200 may also access or interact915 with adaptive computer-basedapplication925 in other ways. The access of, or interaction with,915 the adaptive computer-basedapplication925 byprocess participants200 is analogous to theinteractions182awithcomputer application182 ofFIG. 3. However, a distinguishing feature ofadaptive process900 is that the access orinteraction915 of the adaptive computer-basedapplication925 byprocess participants200 may includeelements1632 of the adaptive computer-basedapplication925 that have been adaptively self-modified905 by the adaptive computer-basedapplication925.

FIG. 4C depicts an extension of theadaptive process900 ofFIG. 4A in which the adaptiverecombinant function850 is combined with the adaptive computer-basedapplication925 to form an adaptive recombinant computer-basedapplication925R. The adaptive recombinant computer-basedapplication925R enables the management of multiple computer-based representations of adaptive process orsub-process instances930, where each process or sub-process representation may be in whole or in part. Further, the adaptive recombinant computer-basedapplication925R enables the management of multiple information structures associated with aspecific process instance930. The management of the representations of process orsub-process instances930 and/or multiple information structures thereof, may include the distribution and combination of the representations of process orsub-process instances930 and/or other information structures, within or across computing systems and/or organizations. These capabilities enable the adaptiverecombinant process901.

For some process applications described herein,adaptive process900 is sufficient to implement the application. Other process applications described herein utilize the additional adaptiverecombinant capabilities850 provided by the adaptiverecombinant process901 for full implementation. Notwithstanding that the term “adaptive recombinant processes” is the general term used herein to describe the present invention, it should be understood that in some process application areas, the additional adaptiverecombinant capabilities850 of the adaptive recombinant process901 (that are extensions to the adaptive process capabilities of the adaptive process900) are not necessary for implementation.

Process Participant Behavior Categories

In Table 1, several differentprocess participant behaviors920, which may also be described as process “usage” behaviors without loss of generality, are identified by the adaptive computer-basedapplication925 and categorized. Theusage behaviors920 may be associated with the entire community of process participants, one or more sub-communities, or with individual process participants or users associated with thesub-process instance930.

TABLE 1


Usage behavior categories and usage behaviors

usage behavior
category	usage behavior examples

navigation and	activity, content and computer application
access	accesses, including buying/selling
	paths of accesses or click streams
subscription and	personal or community subscriptions to
self-profiling	process topical areas
	interest and preference self-profiling
	affiliation self-profiling (e.g., job function)
collaborative	referral to others
	discussion forum activity
	direct communications (voice call, messaging)
	content contributions or structural alterations
reference	personal or community storage and tagging
	personal or community organizing of stored or
	tagged information
direct	user ratings of activities, content, computer
feedback	applications and automatic recommendations
	user comments
attention	direction of gaze
	brain patterns
physical	current location
location	location over time
	relative location to users/object references

A first category ofprocess usage behaviors920 is known as system navigation and access behaviors. System navigation and access behaviors includeusage behaviors920 such as accesses to, and interactions with online computer applications and content such as documents, Web pages, images, videos, audio, multi-media, interactive content, interactive computer applications, e-commerce applications, or any other type of information item or system “object.” These process usage behaviors may be conducted through use of a keyboard, a mouse, oral commands, or using any other input device.Usage behaviors920 in the system navigation and access behaviors category may include, but are not limited to, the viewing or reading of displayed information, typing written information, interacting with online objects orally, or combinations of these forms of interactions with computer-based applications.

System navigation and access behaviors may also include executing transactions, including commercial transactions, such as the buying or selling of merchandise, services, or financial instruments. System navigation and access behaviors may include not only individual accesses and interactions, but the capture and categorization of sequences of information or system object accesses and interactions over time.

A second category ofusage behaviors920 is known as subscription and self-profiling behaviors. Subscriptions may be associated with specific topical areas or other elements of the adaptive computer-basedapplication925, or may be associated with any other subset of the adaptive computer-basedapplication925. Subscriptions may thus indicate the intensity of interest with regard to elements of the adaptive computer-basedapplication925. The delivery of information to fulfill subscriptions may occur online, such as through electronic mail (email), on-line newsletters, XML feeds, etc., or through physical delivery of media.

Self-profiling refers to other direct, persistent (unless explicitly changed by the user) indications explicitly designated by the one or more process participants regarding their preferences and interests, or other meaningful attributes. Aprocess participant200 may explicitly identify interests or affiliations, such as job function, profession, or organization, and preferences, such as representative skill level (e.g., novice, business user, advanced). Self-profiling enables the adaptive computer-basedapplication925 to infer explicit preferences of the process participant. For example, a self-profile may contain information on skill levels or relative proficiency in a subject area, organizational affiliation, or a position held in an organization. Aprocess participant200 that is in the role, or potential role, of a supplier or customer may provide relevant context for effective adaptive e-commerce applications through self-profiling. For example, a potential supplier may include information on products or services offered in his or her profile. Self-profiling information may be used to infer preferences and interests with regard to system use and associated topical areas, and with regard to degree of affinity with other process participant community subsets. A process participant may identify preferred methods of information receipt or learning style, such as visual or audio, as well as relative interest levels in other communities.

A third category ofusage behaviors920 is known as collaborative behaviors. Collaborative behaviors are interactions among the one or more process participants. Collaborative behaviors may thus provide information on areas of interest and intensity of interest. Interactions including online referrals of elements or subsets of the adaptive computer-basedapplication925, such as through email, whether to other process participants or to non-process participants, are types of collaborative behaviors obtained by the adaptive computer-basedapplication925.

Other examples of collaborative behaviors include, but are not limited to, online discussion forum activity, contributions of content or other types of objects to the adaptive computer-basedapplication925, or any other alterations of the elements, objects or relationships among the elements and objects of adaptive computer-basedapplication925. Collaborative behaviors may also include general user-to-user communications, whether synchronous or asynchronous, such as email, instant messaging, interactive audio communications, and discussion forums, as well as other user-to-user communications that can be tracked by the adaptive computer-basedapplication925.

A fourth category ofprocess usage behaviors920 is known as reference behaviors. Reference behaviors refer to the saving or tagging of specific elements or objects of the adaptive computer-basedapplication925 for recollection or retrieval at a subsequent time. The saved or tagged elements or objects may be organized in a manner customizable by process participants. The referenced elements or objects, as well as the manner in which they are organized by the one or more process participants, may provide information on inferred interests of the one or more process participants and the associated intensity of the interests.

A fifth category ofprocess usage behaviors920 is known as direct feedback behaviors. Direct feedback behaviors include ratings or other indications of perceived quality by individuals of specific elements or objects of the adaptive computer-basedapplication925, or the attributes associated with the corresponding elements or objects. The direct feedback behaviors may therefore reveal the explicit preferences of the process participant. In the adaptive computer-basedapplication925, theadaptive recommendations910 may be rated byprocess participants200. This enables a direct, adaptive feedback loop, based on explicit preferences specified by the process participant. Direct feedback also includes user-written comments and narratives associated with elements or objects of the computer-basedsystem925.

A sixth category of process usage behaviors is known as attention behaviors. These behaviors are associated with the focus of attention of process participants and/or the intensity of the intention. For example, the direction of the visual gaze of one or more process participants may be determined. This behavior can inform inferences associated with preferences or interests even when no physical interaction with the adaptive computer-basedapplication925 is occurring. Even more direct assessment of the level of attention may be conducted through access to the brain patterns or signals associated with the one or more process participants. Such patterns of brain functions during participation in a process can inform inferences on the preferences or interests of process participants, and the intensity of the preferences or interests. The brain patterns assessed may include MRI images, brain wave patterns, relative oxygen use, or relative blood flow by one or more regions of the brain.

Attention behaviors may include any other type of physiological response of aprocess participant200 that may be relevant for making preference or interest inferences, independently, or collectively with the other usage behavior categories. Other physiological responses may include, but are not limited to, utterances, gestures, movements, or body position. Attention behaviors may also include other physiological responses such as breathing rate, blood pressure, or galvanic response.

A seventh category of process usage behaviors is known as physical location behaviors. Physical location behaviors identify physical location and mobility behaviors of process participants. The location of a process participant may be inferred from, for example, information associated with a Global Positioning System or any other positionally or locationally aware system or device. The physical location of physical objects referenced by elements or objects of adaptive computer-basedapplication925 may be stored for future reference. Proximity of a process participant to a second process participant, or to physical objects referenced by elements or objects of the computer-based application, may be inferred. The length of time, or duration, at which one or more process participants reside in a particular location may be used to infer intensity of interests associated with the particular location, or associated with objects that have a relationship to the physical location. Derivative mobility inferences may be made from location and time data, such as the direction of the process participant, the speed between locations or the current speed, the likely mode of transportation used, and the like. These derivative mobility inferences may be made in conjunction with geographic contextual information or systems, such as through interaction with digital maps or map-based computer systems.

In addition to the usage behavior categories depicted in Table 1, usage behaviors may be categorized over time and across user behavioral categories. Temporal patterns may be associated with each of the usage behavioral categories. Temporal patterns associated with each of the categories may be tracked and stored by the adaptive computer-basedapplication925. The temporal patterns may include historical patterns, including how recently an element, object or item of content associated with adaptive computer-basedapplication925. For example, more recent behaviors may be inferred to indicate more intense current interest than less recent behaviors.

Another temporal pattern that may be tracked and contribute to preference inferences that are derived is the duration associated with the access or interaction with the elements, objects or items of content of the adaptive computer-basedapplication925, or the user's physical proximity to physical objects referenced by system objects of the adaptive computer-basedapplication925, or the user's physical proximity to other process participants. For example, longer durations may generally be inferred to indicate greater interest than short durations. In addition, trends over time of the behavior patterns may be captured to enable more effective inference of interests and relevancy. Sinceadaptive recommendations910 may include one or more elements, objects or items of content of the adaptive computer-basedapplication925, the usage pattern types and preference inferencing may also apply to interactions of the one or more process participants with theadaptive recommendations910 themselves.

Process Participant Behavior and Usage Framework

FIG. 5 depicts ausage framework1000 for performing preference inferencing of tracked or monitoredusage behaviors920 associated with a process orsub-process instance930 by the adaptive computer-basedapplication925. Theusage framework1000 summarizes the manner in which process usage patterns are managed within the adaptive computer-basedapplication925. Usage behavioral patterns associated with an entire community, affinity group, or segment ofprocess participants1002 are captured by the adaptive computer-basedapplication925. In another case, usage patterns specific to an individual, shown inFIG. 5 asindividual usage patterns1004, are captured by the adaptive computer-basedapplication925. Various sub-communities of usage associated with process participants may also be defined, as for example sub-communityA usage patterns1006, sub-communityB usage patterns1008, and sub-communityC usage patterns1010.

Memberships in the communities are not necessarily mutually exclusive, as depicted by the overlaps of the sub-communityA usage patterns1006, sub-communityB usage patterns1008, and sub-community C usage patterns1010 (as well as and the individual usage patterns1004) in theusage framework1000. Recall that a community may include a single process participant or multiple process participants. Sub-communities may likewise include one or more process participants. Thus, theindividual usage patterns1004 inFIG. 5 may also be described as representing the process usage patterns of a community or a sub-community. For the adaptive computer-basedapplication925, usage behavior patterns may be segmented among communities and individuals so as to effectively enable

adaptive recommendations

910,905 for each sub-community or individual.

The communities identified by the adaptive computer-basedapplication925 may be determined through self-selection, through explicit designation by other process participants or external administrators (e.g., designation of certain process participants as “experts”), or through automatic determination by the adaptive computer-basedapplication925. The communities themselves may have relationships between each other, of multiple types and values. In addition, a community may be composed not of human users, or solely of human users, but instead may include one or more other computer-based systems, which may have reason to interact with the adaptive computer-basedapplication925. Or, such computer-based systems may provide an input into the adaptive computer-basedapplication925, such as by being the output from a search engine. The interacting computer-based system may be another instance of the adaptive computer-basedapplication925.

Theusage behaviors920 included in Table 1 may be categorized by the adaptive computer-basedapplication925 according to theusage framework1000 ofFIG. 5. For example, categories of usage behavior may be captured and categorized according to the entirecommunity usage patterns1002,sub-community usage patterns1006, andindividual usage patterns1004. The corresponding usage behavior information may be used to infer preferences and interests at each of the user levels.

Multiple usage behavior categories shown in Table 1 may be used by the adaptive computer-basedapplication925 to make reliable inferences of the preferences of a process participant with regard to elements, objects, or items of content associated with the adaptive computer-basedapplication925. There are likely to be different preference inferencing results for different process participants. In addition, preference inferencing may be different with regard to optimizing the delivery ofadaptive recommendations910 to process participants than the preference inferencing optimized for modifying thestructure905 of the adaptive computer-basedapplication925, as modifications to the structure are likely to be persistent and affect many process participants.

As an example, simply using the sequences of content accesses as the sole relevant usage behavior on which to base updates to the structure will generally yield unsatisfactory results. This is because the structure itself, through navigational proximity, will create a tendency toward certain navigational access sequence biases. Using just object or content access sequence patterns as the basis for updates to the structural will therefore tend to reinforce the pre-existing structure of the adaptive computer-basedapplication925, which may limit the adaptiveness of the adaptive computer-basedapplication925.

By introducing different or additional behavioral characteristics, such as the duration of access of an item of content, on which to base updates to the structure of adaptive computer-basedapplication925, a more adaptive process is enabled. For example, duration of access will generally be much less correlated with navigational proximity than access sequences will be, and therefore provide a better indicator of true user preferences. Therefore, combining access sequences and access duration will generally provide better inferences and associated system structural updates than using either usage behavior alone. Effectively utilizing additional usage behaviors as described above will generally enable increasingly effective system structural updating. In addition, the adaptive computer-basedapplication925 may employ user affinity groups to enable even more effective system structural updating than are available merely by applying either individual (personal) usage behaviors or entire community usage behaviors.

Furthermore, relying on only one or a limited set of usage behavioral cues and signals may more easily enable potential “spoofing” or “gaming” of the computer-basedapplication925. “Spoofing” or “gaming” the adaptive computer-basedapplication925 refers to conducting consciously insincere or otherwiseintentional usage behaviors920, so as to influence theadaptive recommendations910 oradaptive modifications905 to the intrinsic elements and structure of the adaptive computer-basedapplication925. Utilizing broader sets of system usage behavioral cues and signals may lessen the effects of spoofing or gaming. One or more algorithms may be employed by computer-basedapplication925 to detect such contrived usage behaviors, and when detected, such behaviors may be compensated for by the preference and interest inferencing algorithms of computer-basedapplication925.

In some embodiments, the computer-basedapplication925 may provideprocess participants200 with a means to limit the tracking, storing, or application of theirusage behaviors920. A variety of limitation variables may be selected by theprocess participant200. For example, aprocess participant200 may be able to limit usage behavior tracking, storing, or application by usage behavior category described in Table 1. Alternatively, or in addition, the selected limitation may be specified to apply only to particular user communities orindividual process participants200. For example, aprocess participant200 may restrict the application of the full set of herprocess usage behaviors920 to preference or interest inferences by adaptive computer-basedapplication925 for application to only herself, and make a subset ofprocess behaviors920 available for application to process participants only within her workgroup, but allow none of her process usage behaviors to be applied by computer-basedapplication925 in making inferences of preferences or interests for other process participants.

Process Participant Communities

As described above, a process participant associated with anadaptive process instance930 may be a member of one or more communities of interest, or affinity groups, with a potentially varying degree of affinity associated with the respective communities. These affinities may change over time as interests of theuser200 and communities evolve over time. The affinities or relationships among process participants and communities may be categorized into specific types. An identifiedprocess participant200 may be considered a member of a special sub-community containing only one member, the member being the identified process participant. A process participant can therefore be thought of as just a specific case of the more general notion of process participant or user segments, communities, or affinity groups.

FIG. 6 illustrates the affinities among user communities and how these affinities may automatically or semi-automatically be updated by the adaptive computer-basedapplication925 based on user preferences which are derived fromprocess participant behaviors920. Anentire community1050 is depicted inFIG. 6. The community may extend across organizational, functional, or process boundaries. Theentire community1050 extends acrossprocess A1060 andprocess B1061. Theentire community1050 includessub-community A1064,sub-community B1062,sub-community C1069,sub-community D1065, andsub-community E1070. Aprocess participant1063 who is not part of theentire community1050 is also featured inFIG. 6.

Sub-community B

1062 is a community that has many relationships or affinities to other communities. These relationships may be of different types and differing degrees of relevance or affinity. For example, afirst relationship1066 between sub-community B1062 andsub-community D1065 may be of one type, and asecond relationship1067 may be of a second type. (InFIG. 6, thefirst relationship1066 is depicted using a double-pointing arrow, while thesecond relationship1067 is depicted using a unidirectional arrow.)

The

relationships

1066 and1067 may be directionally distinct, and may have an indicator of relationship or affinity associated with each distinct direction of affinity or relationship. For example, thefirst relationship1066 has anumerical value1068, or relationship value, of “0.8.” Therelationship value1068 thus describes thefirst relationship1066 between sub-community B1062 andsub-community D1065 as having a value of 0.8.

The relationship value may be scaled as inFIG. 6 (e.g., between 0 and 1), or may be scaled according to another interval. The relationship values may also be bounded or unbounded, or they may be symbolically represented (e.g., high, medium, low).

Theprocess participant1063, which could be considered a process participant community including a single member, may also have a number of relationships to other communities, where these relationships are of different types, directions and relevance. From the perspective of theprocess participant1063, these relationship types may take many different forms. Some relationships may be automatically formed by the adaptive computer-basedapplication925, for example, based on interests or geographic location or similar traffic/usage patterns. Thus, for example theentire community1050 may include process participants in a particular city. Some relationships may be context-relative. For example, a community to which theprocess participant1063 has a relationship could be associated with a certain process, and another community could be related to another process. Thus,sub-community E1070 may be the process participants associated with a product development business to which theprocess participant1063 has a relationship1071;sub-community B1062 may be the members of a cross-business innovation process to which theuser1063 has arelationship1073;sub-community D1065 may be experts in a specific domain of product development to which theprocess participant1063 has arelationship1072. The generation of new communities which include theprocess participant1063 may be based on the inferred interests of theprocess participant1063 or other process participants within theentire community1050.

Membership of communities may overlap, as indicated bysub-communities A1064 andC1069. The overlap may result when one community is wholly a subset of another community, such as between theentire community1050 andsub-community B1062. More generally, a community overlap will occur whenever two or more communities contain at least one process participant or user in common. Such community subsets may be formed automatically by theadaptive process900, based on preference inferencing fromprocess participant behaviors920. For example, a subset of a community may be formed based on an inference of increased interest or demand of particular content or expertise of an associated community. The adaptive computer-basedapplication925 is also capable of inferring that a new community is appropriate. The adaptive computer-basedapplication925 of theadaptive process900 will thus create the new community automatically.

For each process participant, whether residing within, say,sub-community A1064, or residing outside thecommunity1050, such as theprocess participant1063, the relationships (such asarrows1066 or1067), affinities, or “relationship values” (such as numerical indicator1068), and directions (of arrows) are unique. Accordingly, some relationships (and specific types of relationships) between communities may be unique to each process participant. Other relationships, affinities, values, and directions may have more general aspects or references that are shared among many process participants, or among all process participants of theadaptive process900. A distinct and unique mapping of relationships between process participants, such as is illustrated inFIG. 6, could thus be produced for each process participant by the adaptive computer-basedapplication925.

The adaptive computer-basedapplication925 may automatically generate communities, or affinity groups, based onprocess participant behaviors920 and associated preference inferences. In addition, communities may be identified by process participants, such as administrators of the process orsub-process instance930. Thus, the adaptive computer-basedapplication925 utilizes automatically generated and manually generated communities in generating

adaptive recommendations

910,905.

The communities, affinity groups, or user segments aid the adaptive computer-basedapplication925 in matching interests optimally, developing learning groups, prototyping process designs before adaptation, and many other uses. For example, some process participants that use or interact with the adaptive computer-basedapplication925 may receive a preview of a new adaptation of a process for testing and fine-tuning, prior to other process participants receiving this change.

The process participants or communities may be explicitly represented as elements or objects within the adaptive computer-basedapplication925. This feature enhances the extensibility and adaptability of theadaptive process900.

Adaptive System

FIG. 7 depicts a possible configuration of the adaptive computer-basedapplication925, as part of theadaptive process900 ofFIGS. 4A and 4B. The adaptive computer-basedapplication925 includes, at least in part, an adaptive system100 (shaded for convenience of identification), according to some embodiments. Theadaptive system100 includes three aspects: 1) astructural aspect210, a usage aspect220, and acontent aspect230. One or more process participants200 (who may also be termed “users” of the adaptive process900) interact with, or are monitored by, theadaptive system100, which tracks selectedbehaviors920 of the process participants, which are in turn selectively stored and processed by the usage aspect220. An adaptive recommendations function240 generates adaptive recommendations based on inputs from the usage aspect220, and, optionally, based on thestructural aspect210 and/or thecontent aspect230. The adaptive recommendations function240 determines inferred interests ofprocess participants200, and generatesadaptive recommendations250 that may be delivered910 to processparticipants200 or may be delivered265 tonon-process participants260. The adaptive recommendations function240 may also apply adaptive recommendations to modify905 thestructural aspect210 or to modify935 thecontent aspect230.

In some embodiments, theadaptive process900 utilizes the methods and systems of adaptive fuzzy network and process models, as defined in U.S. Pat. No. 6,795,826, entitled “Fuzzy Content Network Management and Access,” and PCT Patent Application No. PCT/US04/37176, entitled “Adaptive Recombinant Systems,” filed on Nov. 4, 2004, which are hereby incorporated by reference as if set forth in their entirety.

As used herein, one ormore process participants200 may be a single user or multiple users of the adaptive computer-basedapplication925. As shown inFIG. 8, the one or more process participants orusers200 may receive910 theadaptive recommendations250. Individuals not participating in theprocess260 of theadaptive system100 may also receive265

adaptive recommendations

250 from theadaptive system100.

The process participant oruser200 may be a human entity, a computer system, or a second adaptive system (distinct from the adaptive system100) that interacts with, or otherwise uses the adaptive computer-basedapplication925 and the associatedadaptive system100. The one ormore users200 may include non-human users of theadaptive system100. In particular, one or more other adaptive systems may serve as virtual system “users.” These other adaptive systems may operate in accordance with the architecture of theadaptive system100. Thus, multiple adaptive systems may be mutual users for one another. These adaptive systems may each support the same process, or eachsystem100 may each support different processes.

It should be understood that thestructural aspect210, thecontent aspect230, the usage aspect220, and the recommendations function240 of theadaptive system100, and elements of each, may be contained within one computer, or distributed among multiple computers. Furthermore, one or more non-adaptive computer-basedapplications182 may be modified to comprise one or moreadaptive systems100 by integrating the usage aspect220 and the recommendations function240 with the one or more non-adaptive computer-basedapplications182.

The term “computer system” or the term “system,” without further qualification, as used herein, will be understood to mean either a non-adaptive or an adaptive system. Likewise, the terms “system structure” or “system content,” as used herein, will be understood to refer to thestructural aspect210 and thecontent aspect230, respectively, whether associated with thenon-adaptive system182 or the adaptive computer-basedapplication925, and associatedadaptive system100. The term “system structural subset” or “structural subset,” as used herein, will be understood to mean a portion or subset of thestructural aspect210 of a system.

Structural Aspect

Thestructural aspect210 of theadaptive system100 is depicted in the block diagram ofFIG. 9A. Thestructural aspect210 denotes a collection of system objects212 that are part of theadaptive system100, as well as the relationships among theobjects214. The relationships amongobjects214 may be persistent across user sessions, or may be transient in nature. Theobjects212 may include or reference items of content, such as text, graphics, audio, video, interactive content, or embody any other type or item of information. Theobjects212 may also include references to content, such as pointers. Computer applications, executable code, or references to computer applications may also be stored or referenced asobjects212 in theadaptive system100. The content of theobjects212 is known herein asinformation232. Theinformation232, though part of theobject214, is also considered part of thecontent aspect230, as depicted inFIG. 9B, and as described below.

Theobjects212 may be managed in a relational database, or may be maintained in structures such as flat files, linked lists, inverted lists, hypertext networks, or object-oriented databases. Theobjects212 may include meta-information234 associated with theinformation232 contained within, or referenced by theobjects212.

As an example, in some embodiments, the World-wide Web may be considered a structural aspect, where web pages constitute the objects of the structural aspect and links between web pages constitute the relationships among the objects. Alternatively, or in addition, in some embodiments, the structural aspect may feature objects associated with an object-oriented programming language, and the relationships between the objects associated with the protocols and methods associated with interaction and communication among the objects in accordance with the object-oriented programming language.

The one ormore users200 of theadaptive system100 may be explicitly represented asobjects212 within thesystem100, thereby becoming directly incorporated within thestructural aspect210. The relationships amongobjects214 may be arranged in a hierarchical structure, a relational structure (e.g. according to a relational database structure), or according to a network structure.

Content Aspect

Thecontent aspect230 of theadaptive system100 is depicted in the block diagram ofFIG. 9B. Thecontent aspect230 denotes theinformation232 contained in, or referenced by theobjects212 that are part of thestructural aspect210. Thecontent aspect230 of theobjects212 may include text, graphics, audio, video, and interactive forms of content, such as applets, tutorials, courses, demonstrations, modules, or sections of executable code or computer programs. The one ormore users200 interact with thecontent aspect230.

Theadaptive system100 may enable an item ofinformation232 to be decomposed into other items ofinformation232. For example, a text document could be decomposed into sections, each of which could become separate items ofinformation232. Further, these items of information could then become anobject212; that is, an explicit element of thestructural aspect210. The decomposition process may also generateappropriate relationships214 among the decomposed objects, which also become explicit elements of thestructural aspect210. The recursive decomposition ofinformation232 intoother information232 and associatedobjects212 and corresponding relationships among theobjects214 may continue without limit.

Thecontent aspect230 may be updated or modified935 (FIG. 7) by the adaptive recommendations function240 based, at least in part, on the usage aspect220, including usage behavior metrics. To achieve this, theadaptive system100 may employ the usage aspect, or elements of the usage aspect, of other systems. Such systems may include, but are not limited to, other computer systems, other networks, such as the World Wide Web, multiple computers within an organization, other adaptive systems, or other adaptive recombinant systems. In this manner, thecontent aspect230 benefits from usage occurring in other environments, including other process environments.

Usage Aspect

The usage aspect220 of theadaptive system100 is depicted in the block diagram ofFIG. 9C. Recall fromFIG. 7 that the usage aspect220 tracks or monitorusage behaviors920 ofprocess participants200. The usage aspect220 denotes captured usage information202, further identified as usage behaviors270, and usage behavior pre-processing204. The usage aspect220 thus reflects the tracking, storing, categorization, and clustering of the use and associatedusage behaviors920 of the one or more users orprocess participants200 interacting with theadaptive system100.

The captured usage information202, known also as system usage or system use202, includes any interaction by the one or more process participants orusers200 with the system, or monitored behavior by the one ormore users200. Theadaptive system100 may track and store user key strokes and mouse clicks, for example, as well as the time period in which these interactions occurred (e.g., timestamps), as captured usage information202. From this captured usage information202, theadaptive system100 identifies usage behaviors270 of the one or more process participants200 (e.g., web page access or physical location changes of the process participant). Finally, the usage aspect220 includes usage-behavior pre-processing, in which usage behavior categories246, usage behavior clusters247, and usage behavioral patterns248 are formulated for subsequent processing of the usage behaviors270 by theadaptive system100. Some usage behaviors270 identified by theadaptive system100, as well as usage behavior categories246 designated by theadaptive system100, are listed in Table 1, above, and are described in more detail below.

The usage behavior categories246, usage behaviors clusters247, and usage behavior patterns248 may be interpreted with respect to asingle user200, or tomultiple users200, in which the multiple users may be described herein as a community, an affinity group, or a user segment. These terms are used interchangeably herein. A community is a collection of one or more users, and may include what is commonly referred to as a “community of interest.” A sub-community is also a collection of one or more users, in which members of the sub-community include a portion of the users in a previously defined community. Communities, affinity groups, and user segments are described in more detail, below.

Usage behavior categories246 include types of usage behaviors270, such as accesses, referrals to other users, collaboration with other users, and so on. These categories and more are included in Table 1, above. Usage behavior clusters247 are groupings of one or more usage behaviors270, either within a particular usage behavior category246 or across two or more usage categories. The usage behavior pre-processing204 may also determine new “clusterings” of user behaviors270 in previously undefined usage behavior categories246, across categories, or among new communities. Usage behavior patterns248, also known as “usage behavioral patterns” or “behavioral patterns,” are also groupings of usage behaviors270 across usage behavior categories246. Usage behavior patterns248 are generated from one or more filtered clusters of captured usage information202.

The usage behavior patterns248 may also capture and organize captured usage information202 to retain temporal information associated with usage behaviors270. Such temporal information may include the duration or timing of the usage behaviors270, such as those associated with reading or writing of written or graphical material, oral communications, including listening and talking, or physical location of theprocess participant200. The usage behavioral patterns248 may include segmentations and categorizations of usage behaviors270 corresponding to a single user of the one ormore users200 or according to multiple users200 (e.g., communities or affinity groups). The communities or affinity groups may be previously established, or may be generated during usage behavior pre-processing204 based on inferred usage behavior affinities or clustering. Usage behaviors270 may also be derived from the use orexplicit preferences252 associated with other adaptive or non-adaptive systems.

Adaptive Recommendations Function

Returning toFIG. 7, theadaptive system100 includes an adaptive recommendations function240, which interacts with thestructural aspect210, the usage aspect220, and thecontent aspect230. The adaptive recommendations function240 generatesadaptive recommendations250 based on the application of the usage aspect220, and, optionally, thestructural aspect210 and/or thecontent aspect230. The adaptive recommendations function240 may also optionally apply other contextual information, rules, or algorithms through the application of other computer-based functions residing withinadaptive system100, or through access to, or interaction with, other computer-based functions residing outside ofadaptive system100.

The term “recommendations” associated with the adaptive recommendations function240 is used broadly in theadaptive system100. Theadaptive recommendations250 generated by recommendations function240 may be displayed or otherwise delivered910,265 to a recommendations recipient. As used herein, a recommendations recipient is an entity who receives theadaptive recommendations250. Thus, the recommendations recipient may include the one ormore process participants200 of theadaptive system100, as indicated by the dottedarrow910 inFIG. 7, or anon-participant260 of the associated process (see dotted arrow265). However, the adaptive recommendations function240 may also be applied internally by theadaptive system100 to update the structural aspect210 (see dotted arrow905). In this manner, the usage behavior270 of the one ormore process participants200 may be influenced by the system structural alterations that are automatically or semi-automatically applied. Or, the adaptive recommendations function240 may be used by theadaptive system100 to update the content aspect230 (see dotted arrow935).

FIG. 10 is a block diagram of the adaptive recommendations function240 used by theadaptive system100 ofFIG. 7. The adaptive recommendations function240 includes two algorithms, apreference inferencing algorithm242 and arecommendations optimization algorithm244. These algorithms (which actually may include many more than two algorithms) are used by theadaptive system100 to generateadaptive recommendations250.

Preferably, theadaptive system100 identifies the preferences of theuser200 and self-adapts theadaptive system100 in view of the preferences. Preferences describe the likes, tastes, partiality, and/or predilection of theuser200 that may be inferred during access of, interaction with, or while attention is directed to, theobjects212 of theadaptive system100. In general, user preferences exist consciously or sub-consciously within the mind of the user. Since theadaptive system100 has no direct access to these preferences, they are generally inferred by thepreference inferencing algorithm242 of the adaptive recommendations function240.

Thepreference inferencing algorithm242, infers preferences based, at least in part, on information that may be obtained as theprocess participant200 accesses theadaptive system100. Additional information may also be optionally used by thepreference inferencing algorithm242, including meta-information234 andintrinsic information232 withinobjects212, and from information, rules, or algorithms accessed from other computer-based functions residing within theadaptive system100, or through access to, or interaction with, other computer-based functions residing outside of theadaptive system100.

The preference inferencing algorithm and associatedoutput242 is also described herein generally as “preference inferencing” or “preference inferences” of theadaptive system100. Thepreference inferencing algorithm242 identifies three types of preferences:explicit preferences252,inferred preferences253, andinferred interests254. Unless otherwise stated, the use of the term “preferences” herein is meant to include any or all of the

elements

252,253, and254 depicted inFIG. 10.

As used herein,explicit preferences252 describe explicit choices or designations made by theuser200 during use of theadaptive system100. Theexplicit preferences252 may be considered to more explicitly reveal preferences than inferences associated with other types of usage behaviors. A response to a survey is one example whereexplicit preferences252 may be identified by theadaptive system100.

Inferred preferences

253 describe preferences of theuser200 that are based on usage behavioral patterns248.Inferred preferences253 are derived from signals and cues made by theprocess participant200, where “signals” are consciously intended communications by the process participant, and “cues” are behaviors that are not intended as explicit communications, but nevertheless provide information of a process participant with which to infer preferences and interests.

Inferred interests

254 describe interests of theuser200 that are based on usage behavioral patterns248. In general, theadaptive recommendations250 generated by the adaptive recommendations function240 are derived from thepreference inferencing algorithm242 and combine inferences from overall user community behaviors and preferences, inferences from sub-community or expert behaviors and preferences, and inferences from personal user behaviors and preferences. As used herein, preferences (whether explicit252 or inferred253) are distinguishable from interests (254) in that preferences imply a ranking (e.g., object A is better than object B) while interests do not necessarily imply a ranking.

Asecond algorithm244, designatedrecommendations optimization244, optimizes theadaptive recommendations250 generated by the adaptive recommendations function240 within theadaptive system100. Theadaptive recommendations250 may be augmented by automated inferences and interpretations about the content within individual and sets ofobjects232 using statistical pattern matching of words, phrases or representations, in written or audio format, or in pictorial format, within the content. Such statistical pattern matching may include, but is not limited to, principle component analysis, semantic network techniques, Bayesian analytical techniques, neural network-based techniques, support vector machine-based techniques, or other statistical analytical techniques.

Adaptive Recommendations

As shown inFIG. 7, theadaptive system100 generatesadaptive recommendations250 using the adaptive recommendations function240. Theadaptive recommendations250, or suggestions, enable users to more effectively use and navigate through theadaptive system100.

Theadaptive recommendations250 are presented as structural subsets of thestructural aspect210.FIG. 11 depicts a hypotheticalstructural aspect210, including a plurality ofobjects212 and associatedrelationships214. The adaptive recommendations function240 generatesadaptive recommendations250 based on usage of thestructural aspect210 by the one ormore process participants200, possibly in conjunction with considerations associated with thestructural aspect210 and thecontent aspect230.

Three

structural subsets

280A,280B, and280C (collectively, structural subsets280) are depicted. Thestructural subset280A includes threeobjects212 and two associated relationships, which are reproduced by the adaptive recommendations function240 in the same form as in the structural aspect210 (objects are speckle shaded). Thestructural subset280B includes a single object (object is shaded), with no associated relationships (even though the object originally had a relationship to another object in the structural aspect210).

The thirdstructural subset280C includes five objects (striped shading), but the relationships between objects has been changed from their orientation in thestructural aspect210. In thestructural subset280C, arelationship282 has been eliminated while anew relationship284 has been formed by the adaptive recommendations function240. The structural subsets280 depicted inFIG. 11 represent but three of a myriad of possible structural subsets that may be derived from the original network of objects by the adaptive recommendations function240.

The illustration inFIG. 11 shows a simplified representation of structural subsets280 being generated fromobjects212 andrelationships214 of thestructural aspect210. Although not shown, the structural subset280 may also include corresponding associated subsets of the usage aspect220, such as usage behaviors and usage behavioral patterns. As used herein, references to structural subsets280 are meant to include the relevant subsets of the usage aspect, or usage subsets, as well.

Theadaptive recommendations250 may be in the context of a currently conducted activity or behavior detected by theadaptive system100, a currently accessedobject232, or a communication with anotherprocess participant200 or non-participant in theprocess260. Theadaptive recommendations250 may also be in the context of a historical path of executed system activities, accessedobjects212, or communications during a specific user session or across user sessions. Theadaptive recommendations250 may be without context of a current activity, currently accessedobject212, current session path, or historical session paths.Adaptive recommendations250 may also be generated in response to direct user requests or queries. Such user requests may be in the context of a current system navigation, access or activity, or may be outside of any such context.

Adaptive recommendations

250 generated by the adaptive recommendations function240 may combine inferences from community, sub-community (including expert), and personal behaviors and preferences, as discussed above, to deliver to the one ormore process participants200, one or more system structural subsets280. Theprocess participants200 may find the structural subsets particularly relevant given the current navigational context of the user within the system, the physical location of the user, and/or a response to an explicit request of the system by the one or more users. In other words, theadaptive recommendation function240 determines preference “signals” from the “noise” of system usage behaviors.

The sources of user behavioral information, which typically include theobjects212 referenced by theuser200, may also include theactual information232 contained therein. In generatingadaptive recommendations250, theadaptive system100 may thus employ search algorithms that use text matching or more general statistical pattern matching to provide inferences on the inferred themes of theinformation232 embedded in, or referenced by, individual objects212. Furthermore, thestructural aspect210 may itself inform the specificadaptive recommendations250 generated. For example, existing relationship structures within thestructural aspect210 at the time of theadaptive recommendations250 may be combined with the user preference inferences based on usage behaviors, along with any inferences based on the content aspect230 (the information232).

Delivery of Adaptive Recommendations

FIG. 12 is a flow diagram showing howadaptive recommendations250 are delivered by theadaptive system100. Recall fromFIG. 7 thatadaptive recommendations250 may be delivered directly to the one or more users200 (dotted arrow910), or the adaptive recommendations function240 may be applied to automatically or semi-automatically update the structural aspect210 (dotted arrow905) or the content aspect230 (dotted arrow935), oradaptive recommendations250 may be delivered directly to the non-user260 of the adaptive system100 (dotted arrow265).

Theadaptive system100 begins by determining the relevant usage behavioral patterns248 (FIG. 9C) to be analyzed (block283). Theadaptive system100 thus identifies the relevant communities, affinity groups, or user segments of the one ormore process participants200. Affinities are then inferred amongobjects212, structural subsets280, and among the identified affinity groups (block284). This data enables the adaptive recommendations function240 to generateadaptive recommendations250 for multiple application purposes. Theadaptive system100 next determines whether the adaptive recommendations function240 will generaterecommendations250 to be delivered directly to the recommendations recipients (e.g.,910 to process

participants

200 or265 to non-participants260), or are to be used to update the adaptive system100 (e.g.,905 to the

structural aspect

210 or935 to the content aspect230) (block285). Where the recommendations recipients are to directly receive the adaptive recommendations (the “no” prong of block285), theadaptive recommendations250 are generated based on mapping the context of the current system use (or “simulated” use if the current context is external to the actual use of the system) (block286) to the usage behavior patterns248 generated by the preference inferencing algorithm242 (block286).

Adaptive recommendations are then delivered visually and/or in other communications forms, such as audio, to the recommendations recipients (block287). The recommendations recipients may be individual users or a group of users, or may benon-users260 of theadaptive system100. For Internet-based applications, theadaptive recommendations250 may be delivered through a web browser directly, or through RSS/Atom feeds and other similar protocols.

Where, instead,adaptive system100 itself is to be the “recipient” of the adaptive recommendations (the “yes” prong of block285), the adaptive recommendations function240 applies the adaptive recommendations to update the structural aspect210 (905) or the content aspect230 (935). Theadaptive recommendations250 generated by the adaptive recommendations function240 are determined based, at least in part, on mapping potential configurations of thestructural aspect210 orcontent aspect230 to the affinities generated by the usage behavioral inferences (block288). The

adaptive recommendations

905 or935 are then delivered to enable updating of thestructural aspect210 or the content aspect230 (block289), respectively.

The adaptive recommendations function240 may operate completely automatically, performing in the background and updating thestructural aspect210 independent of human intervention. Or, the adaptive recommendations function240 may be used by users or experts who rely on theadaptive recommendations250 to provide guidance in maintaining the system structure as a whole, or maintaining specific structural subsets280 (semi-automatic maintenance of the structural aspect210).

The navigational context for therecommendation250 may be at any stage of navigation of the structural aspect210 (e.g., during the viewing of a particular object212) or may be at a time when the recommendation recipient is not engaged in directly navigating thestructural aspect210. In fact, the recommendation recipient need not have explicitly used the system associated with therecommendation250.

Some inferences will be weighted as more important than other inferences in generating therecommendation250. These weightings may vary over time, and across recommendation recipients, whether individual recipients or sub-community recipients. As an example, the characteristics associated withobjects212 which are explicitly stored or tagged by theuser200 in a personalstructural aspect210 would typically be a particularly strong indication of preference as storing or tagging system structural subsets requires explicit action by theuser200. Therecommendations optimization algorithms244 may thus prioritize this type of information to be more influential in driving theadaptive recommendations250 than, say, general community traffic patterns within thestructural aspect210.

Therecommendations optimization algorithm244 will particularly try to avoid recommendingobjects212 that the process participant oruser200 is already familiar with. For example, if theprocess participant200 has already stored or tagged theobject212 in a personal structural subset280, then theobject212 may be a low ranking candidate for recommendation to the user, or, if recommended, may be delivered to the user with a designation acknowledging that the user has already saved or marked the object for future reference. Likewise, if theuser200 has recently already viewed the associated system object (regardless of whether it was saved to his personal system), then the object would typically rank low for inclusion in a set of recommended objects.

Thepreference inferencing algorithm242 may be tuned by the individual user. The tuning may occur asadaptive recommendations250 are provided to the user, by allowing the user to explicitly rate the adaptive recommendations. Theuser200 may also set explicit recommendation tuning controls to adjust the adaptive recommendations to her particular preferences. For example, theuser200 may guide the adaptive recommendations function240 to place more relative weight on inferences of expert preferences versus inferences of the user's own personal preferences. This may particularly be the case if the user was relatively inexperienced in the corresponding domain of knowledge associated with thecontent aspect230 of the system, or a structural subset280 of the system. As the user's experience grows, she may adjust the weighting toward inferences of the user's personal preferences versus inferences of expert preferences.

Adaptive recommendations, which are structural subsets of the adaptive system100 (seeFIG. 11), may be displayed in variety of ways to the user. The structural subsets280 may be displayed as a list of objects212 (where the list may be null or a single object). The structural subset280 may be displayed graphically. The graphical display may provide enhanced information that may include depicting relationships among objects (as in the “relationship” arrows ofFIG. 6).

In addition to the structural subset280, the recommendation recipient may be able to access information or logic to assist in gaining an understanding about why the particular structural subset was selected as the recommendation to be presented to the user. The reasoning may be fully presented to the recommendation recipient as desired by the recommendation recipient, or it may be presented through a series of interactive queries and associated answers, where the recommendation recipient desires more detail. The reasoning may be presented through display of the logic of therecommendations optimization algorithm244. A natural language (e.g., English) interface may be employed to enable the reasoning displayed to the user to be as explanatory and human-like as possible.

The personal preference of the user may affect the nature of the display of the information. For example some users may prefer to see the structural aspect in a visual, graphic format while other users may prefer a more interactive question and answer or textual display.

Users of theadaptive system100, and by extension,process participants200, may be explicitly represented as objects in thestructural aspect210 and hence embodied in structural subsets280. Either embodied as structural subsets or represented separately from structural subsets280, theadaptive recommendations250 may include a set of users of theadaptive system100 that are determined and displayed to recommendation recipients, providing either implicit or explicit permission is granted by the set of users to be included in theadaptive recommendations250. Therecommendations optimization algorithm244 may match the preferences of other users of the system with the current user. The preference matching may include applying inferences derived from the characteristics of structural subsets stored or tagged by users, their structural subset subscriptions and other self-profiling information, and their system usage patterns248. Information about the recommended set of users may be displayed. This information may include names, as well as other relevant information such as affiliated organization and contact information. The information may also include system usage information, such as common system objects subscribed to, etc. As in the case of structural subset adaptive recommendations, the adaptive recommendations of other users may be tuned by an individual user through interactive feedback with theadaptive system100.

Theadaptive recommendations250 may be in response to explicit requests from the user. For example, a user may be able to explicitly designate one ormore objects212 or structural subsets280, and prompt theadaptive system100 for a recommendation based on the selected objects or structural subsets. Therecommendations optimization algorithm244 may put particular emphasis on the selected objects or structural subsets, in addition to applying inferences on preferences from usage behaviors, as well as optionally, content characteristics.

In some embodiments, the adaptive recommendations function240 may augment thepreference inferencing algorithm242 with considerations related to enhancing the revelation of user preferences, so as to better optimize theadaptive recommendations250 in the future. In other words, where the value of information associated with reducing uncertainty associated with user preferences is high, the adaptive recommendations function240 may choose to recommendobjects212 or other recommendedstructural aspects210 as an “experiment.” For example, the value of information will typically be highest for relatively new users, or when there appears to be a significant change in usage behavioral pattern248 associated with theuser200. The adaptive recommendations function240 may employ design of experiment (DOE) algorithms so as to select the best possible “experimental” adaptive recommendations, and to optimally sequence such experimental adaptive recommendations, and to adjust such experiments as additional usage behaviors270 are assimilated. In some embodiments, the adaptive recommendations function240 may apply methods and systems disclosed in U.S. Provisional Patent Application Ser. No. 60/652,578, entitled “Adaptive Decision Process,” filed Feb. 14, 2005, which is incorporated by reference herein, as if set forth in its entirety.

Thepreference inferencing242 andrecommendations optimization244 algorithms may also preferentially deliver content that is specially sponsored; for example, promotional, advertising or public relations-related content.

In summary, the adaptive recommendations generated by the adaptive recommendations function240 may be delivered910 to theusers200, delivered265 to the non-user260, or delivered905,935 back to theadaptive system100, for updating either the structural aspect210 (905) or the content aspect230 (935). Theadaptive recommendations250 generated by the adaptive recommendations function240 will thus influence subsequent user interactions and behaviors associated with theadaptive system100, creating a dynamic feedback loop.

Automatic or Semi-Automatic System Structure Maintenance

The adaptive recommendations function240, optionally in conjunction with system structure maintenance functions that reside within, or are accessible by, the adaptive computer-based application925 (not shown), may be used to automatically or semi-automatically update and enhance thestructural aspect210 of theadaptive system100. The adaptive recommendations function240 may be employed to determinenew relationships214, or modify existingrelationships214, amongobjects212 in the adaptive system, within structural subsets280, or among structural subsets associated with a specific sub-community. The automatic updating may include potentially assigning a relationship between any two objects to zero (effectively deleting the relationship between the two objects). The modified relationships may represent the workflow sequencing among objects within thestructural aspect210, where objects represent a process, sub-process or activity.

In either an autonomous mode of operation, or in conjunction with human expertise, the adaptive recommendations function240 may be used to integratenew objects212 into thestructural aspect210, or to delete existingobjects212 from the structural aspect.

The adaptive recommendations function240 may also be extended to scan and evaluate structural subsets280 that have special characteristics. For example, the adaptive recommendations function240 may suggest that certain of the structural subsets that have been evaluated are candidates for special designation. This may include being a candidate for becoming a new specially designated sub-system or structural subset. The adaptive recommendations function240 will present to human users or experts the structural subset280 that is suggested to become a new sub-system or structural subset, along with existing sub-system or structural subsets that are deemed “closest” in relationship to the new suggested structural subset. A human user or expert may then be invited to add the object orobjects212, and may manually createrelationships214 between the new object and existing objects.

As another alternative, the adaptive recommendations function240, optionally in conjunction with the system structure maintenance functions, may automatically generate the object orobjects212, and may automatically generate therelationships214 between the newly created object andother objects212 in thestructural aspect210.

This capability is extended such that the adaptive recommendations function240, in conjunction with system structure maintenance functions, automatically maintains the structural aspect and identified structural subsets280. The adaptive recommendations function240 may identifynew objects212, generate associatedobjects212, and generate associatedrelationships214 among thenew objects212 and existingobjects212, but also may identifyobjects212 that are candidates for deletion. The adaptive recommendations function240 may also automatically delete theobject212 and its associatedrelationships214.

The adaptive recommendations function240, in conjunction with system structure maintenance functions, may apply “global” considerations and logic when conducting modifications to thestructural aspect210 to ensure effective use and navigation of thestructural aspect210. For example, thresholds or limits may guide the absolute number or relative number of relationships among objects. Similarly, rules may be applied to the number of elements in thestructural aspect210 as a whole, or within designated subsets ofstructural aspect210. Rules related to the duration anobject212 has been incorporated within thestructural aspect210, or collective quality thresholds forobjects212 may also be applied. These global rules help ensure thatadaptive system100 performs at an optimum possible level of efficiency and effectiveness forprocess participants200 collectively, according to some embodiments.

In this way the adaptive recommendations function240, optionally in conjunction with a system structure maintenance function, may automatically adapt thestructural aspect210 of theadaptive system100, whether on a periodic or continuous basis, so as to optimize the user experience.

In some embodiments, each of the automatic steps listed above with regard to updating thestructural aspect210 may be employed interactively by human users and experts as desired.

Hence, the adaptive recommendations function240, driven in part by usage behaviors, automatically or semi-automatically updates the system structural aspect210 (seedotted arrow905 inFIG. 7). The feedback loop is closed as process participant interactions with theadaptive system100 are influenced by thestructural aspect210, providing an adaptive, self-reinforcing feedback loop between theusage aspect230 and thestructural aspect210.

Automatic or Semi-Automatic System Content Maintenance

As shown inFIG. 7, the adaptive recommendations function240 may provide the ability to automatically or semi-automatically update thecontent aspect230 of the adaptive system100 (see dotted arrow935). Examples of on-line content orinformation232 within thecontent aspect230 that may be updated or modified include text, animation, audio, video, tutorials, manuals, executable code, and interactive applications. Further, meta-information234, such as reviews and brief descriptions of the content may also be updated or modified935.

The contentaspect information items232 may be directly modified235 by the adaptive recommendations function240. Following are some illustrative examples. For text-basedinformation232, words or phrases may be altered, alternative languages may be applied, and/or the formatting ofinformation232 may be altered235. Hyperlinks may be added or deleted to text-basedinformation232. For image or graphical-basedinformation232, images may be altered235, or formatting such as color may be adjusted235. For audio-based or video-basedinformation232, alternative languages may be applied235 and/or alternative sound tracks may be applied235.

Advertising or promotional elements may be added, deleted, or adjusted withininformation232.

Customized text or multi-media content suitable for online viewing or printing may be generated and stored235 in thecontent aspect230. U.S. patent application Ser. No. 10/715,174 entitled “A Method and System for Customized Print Publication and Management” discloses relevant approaches for updating thecontent aspect230 with adaptive print media instances and is incorporated by reference herein, as if set forth in its entirety.

The adaptive recommendations function240 may operate automatically, performing in the background and updating thecontent aspect230 independently of human intervention. Or, the adaptive recommendations function240 may be used byusers200 or special experts who rely on theadaptive recommendations250 to provide guidance in maintaining thecontent aspect230.

As in the case of thestructural aspect210, different communities may also be used to model the maintenance of thecontent aspect230. The communities, affinity groups, and user segments are used to adapt the relevancies and to create, alter or deleterelationships214 between theobjects212. Theadaptive recommendations250 may present theobjects212 to theuser200 in a different combination than initially may have been assembled or inputted, and may treat sections of asuperordinate object212 such as a document, book, manual, video, sound track, or interactive software as multiplesubordinate objects212 that can be recombined in a pattern that is aligned with community usage, by creating or altering relationships between sections of thesuperordinate object212.

In addition, as user feedback on system activities and usage behavioral patterns248 is accumulated, theadaptive system100 may suggest areas where additional content would be beneficial to users. For example, if theobject212 is frequently rated byusers200 as difficult to understand, or if only expert users in a community are accessing the object, theadaptive system100 may recognize the need for generating supplemental content (e.g., in the form of documentation or online tutorials or demonstrations), and/or a need to re-structureobject212 and/or the associated meta-information234 orinformation232.

The re-structuring935 of theobject212 may include decomposing the associated meta-information234 orinformation232 intosubordinate objects212, and/or meta-information234 orinformation232, and applyingappropriate relationships214 to these newly created elements.

Hence, as shown inFIG. 7, the adaptive recommendations function240, driven in part by usage behaviors270 (seeFIG. 9C), automatically orsemi-automatically updates935 thecontent aspect230. The feedback loop is closed as the interactions of theuser200 with theadaptive system100 are influenced by updates to thecontent aspect230, providing an adaptive, self-reinforcing feedback loop between theusage aspect210 and thecontent aspect230, and, in some embodiments, between theusage aspect210, the structural aspect220, and thecontent aspect230.

Network-Based Embodiments

Thestructural aspect210 of theadaptive system100 may be based on a network structure. Thestructural aspect210 thus includes two or more objects, along with associated relationships among the objects. Networks, as used herein, are distinguished from other structures, such as hierarchies, in that networks allow potential relationships between any two objects of a collection of objects. In a network, there does not necessarily exist well-defined parent objects, and associated children, grandchildren, etc., objects, nor a “root” object associated with the entire system, as there would be by definition in a hierarchy. In other words, networks may include cyclic relationships that are not permitted in strict hierarchies. As used herein, a hierarchy can be thought of as just one particular form of a network, with some additional restrictions on relationships among network objects.

Thestructural aspect210 of theadaptive system100 may also have a fuzzy network structure. Fuzzy networks are distinguished from other types of network structures in that the relationships between objects in fuzzy networks may be by degree. In non-fuzzy networks, the relationships between objects are binary. Thus, in non-fuzzy networks, between any two objects relationships either exist or they do not exist.

As used herein, a fuzzy network is defined as a network of information in which each individual item of information may be related to any other individual item of information, and the associated relationship between the two items may be by degree. A fuzzy network can be thought of abstractly as a manifestation of relationships among fuzzy sets (rather than classical sets), hence the designation “fuzzy network.” As used herein, a non-fuzzy network is a subset of a fuzzy network, in which relationships are restricted to binary values (i.e., relationship either exists or does not exist.

Generalizing further, both classical networks and fuzzy networks may have a-directional (also called non-directed) or directed links between nodes. Four network topologies are listed in Table 2.

TABLE 2


Network Topologies

network type	links between nodes	link type

type i (classical)	binary	a-directional
type ii (classical)	binary	distinctly directional
type iii (fuzzy)	multi-valued	a-directional
type iv (fuzzy)	multi-valued	distinctly directional

The first two types (i and ii) are classical networks. Fuzzy networks, as used herein, are networks with topologies iii or iv.

For each of the four network topologies listed in Table 2, another possible variation exists: whether the network allows only a single link or multiple links between any two nodes, where the multiple links may correspond to multiple types of links. For example, the fuzzy network types (iii and iv) of Table 2 may permit multiple directionally distinct and multi-valued links between any two nodes in the network. Theadaptive system100 encompasses any of the network topologies listed in Table 2, including those which allow multiple links and multiple link types between nodes.

Mathematically, for a non-fuzzy network, it can be said, without loss of generality, that a relationship translates to either a “0” or a “1”-“0,” for example if there is not a relationship, and “1” if there is a relationship. For fuzzy networks, the relationships between any two nodes, when normalized, may have values along a continuum between 0 and 1 inclusive, where 0 implies no relationship between the nodes, and 1 implies the maximum possible relationship between the nodes.

Thestructural aspect210 of theadaptive system100 ofFIG. 7 may support any of the network topologies described above. A-directional relationships between nodes (no arrows), directed relationships between nodes (whether single- or double-arrow), and multiple types of relationships between nodes, are supported by theadaptive system100. Further, relationship indicators which are binary (e.g., 0 or 1) or multi-valued (e.g., range between 0 and 1) are supported by the adaptive system.

It can readily be seen that a hierarchy may be described as a directed fuzzy network with the additional restrictions that the relationship values and indicators associated with each relationship must be either “1” or “0” (or the symbolic equivalent). Further, hierarchies do not support cyclic or closed relationship paths.

FIG. 13 illustrates afuzzy network500, including asubset502 offuzzy network500. Thesubset502 includes three

objects

504,506, and508, designated as shaded for ease of identification. Thesubset502 also includes associated relationships (arrows) and relationship indicators or weightings (values) among the three objects. The separated subset of thenetwork502 yields a fuzzy network (subset)500s.

A particular implementation of a fuzzy network structure, a fuzzy content network, which may advantageously constitute thefuzzy network500, is disclosed in U.S. Pat. No. 6,795,826, entitled “Fuzzy Content Network Management and Access,” and is incorporated by reference herein, as if set forth in its entirety.

Theadaptive system100 ofFIG. 7 may utilize fuzzy network structures, such as thefuzzy network500 ofFIG. 13. InFIG. 14, anadaptive system100C includes astructural aspect210C that is afuzzy network500. Thus,adaptive recommendations250 generated by theadaptive system100C are also structural subsets that are themselves fuzzy networks. Further, although not explicitly shown inFIG. 14, the usage aspect220 may also be entirely, or in part, represented by a fuzzy network.

Thestructural aspect210 of theadaptive system100 may include multiple types of structures, comprising network-based structures, non-network-based structures, or combinations of network-based structures and non-network-based structures. InFIG. 15, theadaptive system100D includes astructural aspect210D, which includes multiple network-based structures and non-network-based structures. The multiple structures of210D may reside on the same computer system, or the structures may reside on separate computer systems.

Adaptive Recombinant Systems

InFIG. 16, according to some embodiments, a particular configuration of the adaptive recombinant computer-basedapplication925R (FIG. 4C) is depicted, in which the adaptive recombinant computer-basedapplication925R includes an adaptiverecombinant system800. The adaptiverecombinant system800 includes theadaptive system100 ofFIG. 7, as well as the adaptiverecombinant function850. The adaptiverecombinant function850 includes asyndication function810, a fuzzy network operators function820, and anobject evaluation function830. Just as theadaptive system100 may be part of theadaptive process900, the adaptiverecombinant system800 may be part of the adaptiverecombinant process901. The adaptiverecombinant function850, including thesyndication function810, the fuzzy network operators function820, and theobject evaluation function830 functions may all reside within the adaptive recombinant computer-basedapplication925R, as shown inFIG. 16, or one or all of the functions may be external to the computer-basedapplication925R.

The adaptiverecombinant system800 is capable of syndicating and recombining structural subsets280. The structural subsets280 may be derived through either direct access of thestructural aspect210 by the fuzzy network operators function820, or the structural subsets280 may be generated by the adaptive recommendations function240. The adaptiverecombinant system800 ofFIG. 16 is capable of syndicating (sharing) and recombining the structural subsets, whether for display to theuser200 or non-user260, or to update thestructural aspect210 and/or thecontent aspect230 of theadaptive system100. In addition, these functions are capable of accessing and updating multipleadaptive systems100, or aiding in the generation of a newadaptive system100.

Thesyndication function810 may syndicate elements of the usage aspect220 associated with syndicated structural subsets280, thus enabling elements of the usage clusters and patterns, along with the corresponding structural subsets, to be combined with other structural subsets and associated usage clusters and patterns.

As explained above, thestructural aspect210 of theadaptive system100 may employ a network structure, and is not restricted to a particular type of network. In some embodiments, the adaptiverecombinant system800 operates in conjunction with an adaptive system in which thestructural aspect210 is a fuzzy network. The structural subsets280 generated by the adaptiverecombinant system800 during syndication or recombination are likewise fuzzy networks in these embodiments, and are also called adaptive recombinant fuzzy networks. Recall that a structural subset is a portion or subset of thestructural aspect210 of theadaptive system100. The structural subset280 may include a single object, or multiple objects, and, optionally, their associated relationships.

The adaptiverecombinant system800 ofFIG. 16 is able to syndicate and combine structural subsets280 of the structural aspect210 (where a structural subset280 may contain the entire structural aspect210). The structural subsets280, which are fuzzy networks, in some embodiments, may be syndicated in whole or in part to other computer networks, physical computing devices, or in a virtual manner on the same computing platform or computing network. Although the adaptiverecombinant system800 is not limited to generating structural subsets which are fuzzy networks, some of the following figures and descriptions, used to illustrate the concepts of syndication and recombination, feature fuzzy networks. Designers of ordinary skill in the art will recognize that the concepts of syndication and recombination may be generalized to other types of networks.

Thus, the adaptiverecombinant system800 ofFIG. 16 may utilize fuzzy network structures. InFIG. 17, an adaptiverecombinant system800C includes theadaptive system100C ofFIG. 14, in which thestructural aspect210C is a fuzzy network. Thus, the adaptiverecombinant system800C may perform syndication and recombination operations, as described above, to generate structural subsets that are fuzzy networks.

Fuzzy Network Subsets and Adaptive Operators

The adaptiverecombinant system800 ofFIG. 16 includesfuzzy network operators820. Thefuzzy network operators820 may manipulate one or more fuzzy or non-fuzzy networks. Some of theoperators820 may incorporate usage behavioral inferences associated with the fuzzy networks that the operators act on, and therefore these operators may be termed “adaptive fuzzy network operators.” Thefuzzy network operators820 may apply to any fuzzy network-based system structure, including fuzzy content network system structures, described further below.

FIG. 18 is a block diagram depicting somefuzzy network operators820, also called functions or algorithms, used by the adaptiverecombinant system800. Aselection operator822, aunion operator824, anintersection operator826, adifference operator828, and acomplement operator832 are included, although additional logical operations may be used by the adaptiverecombinant system800. Additionally, thefuzzy network operators820 include aresolution function834, which is used in conjunction with one or more of the operators in thefuzzy network operators820.

Aselection operator822, which selects subsets of networks, may designate the selected network subsets based on degrees of separation. For example, subsets of a fuzzy network may be selected from the neighborhood, around a given node, say Node X. The selection may take the form of selecting all nodes within the designated network neighborhood, or all the nodes and all the associated links as well within the designated network neighborhood, where the network neighborhood is defined as being within a certain degree of separation from Node X. A non-null fuzzy network subset will therefore contain at least one node, and possibly multiple nodes and relationships.

Two or more fuzzy network subsets may then be operated on by network operations such as union, intersection, difference, and complement, as well as any other network operators that are analogous to Boolean set operators. An example is an operation that outputs the intersection (intersection operator826) of the network subset defined by the first degree or less of separation from Node X and the network subset defined by the second or less degree of separation from Node Y. The operation would result in the set of nodes and relationships common to these two network subsets, with special auxiliary rules optionally applied to resolve duplicative relationships as explained below.

Thefuzzy network operators820 may have special capabilities to resolve the situation in whichunion824 andintersection826 operators define common nodes, but with differing relationships or values of the relationships among the common nodes. The fuzzynetwork intersection operator826, Fuzzy_Network_Intersection, may be defined as follows:
Z=Fuzzy_Network_Intersection(X,Y,W)
where X, Y, and Z are network subsets and W is theresolution function834. Theresolution function834 designates how duplicative relationships among nodes common to fuzzy network subsets X and Y are resolved.

Specifically, the fuzzynetwork intersection operator826 first determines the common nodes of network subsets X and Y, applying theobject evaluation function830 to determine the degree to which nodes are identical, to form a set of nodes, network subset Z. The fuzzynetwork intersection operator826 then determines the relationships and associated relationship value and indicators uniquely deriving from X among the nodes in Z (that is, relationships that do not also exist in Y), and adds them into Z (attaching them to the associated nodes in Z). The operator then determines the relationships and relationship indicators and associated values uniquely deriving from Y (that is, relationships that do not also exist in X) and applies them to Z (attaching them to the associated nodes in Z).

For relationships that are common to X and Y, theresolution function834 is applied. Theresolution function834 may be any mathematical function or algorithm that takes the relationship values of X and Y as arguments, and determines a new relationship value and associated relationship indicator.

Theresolution function834, Resolution_Function, may be a linear combination of the corresponding relationship value of X and the corresponding relationship value of Y, scaled accordingly. For example:
Resolution_Function(X_RV,Y_RV)=(c₁*X_RV+c₂*Y_RV)/(c₁+c₂)
where X_RVand Y_RVare relationship values of X and Y, respectively, and c₁and c₂are coefficients. If c₁=1, and c₂=0, then X_RVcompletely overrides Y_RV. If c₁=0 and c₂=1, then Y_RVcompletely overrides X_RV. If c₁=1 and c₂=1, then the derived relationship is a simple average of X_RVand Y_RV. Other values of c₁and c₂may be selected to create weighted averages of X_RVand Y_RV. Nonlinear combinations of the associated relationships values, scaled appropriately, may also be employed.

TheFuzzy_Network_Union operator824 may be derived from theFuzzy_Network_Intersection operator826, as follows:
Z=Fuzzy_Network_Union(X,Y,W)
where X, Y, and Z are network subsets and W is theresolution function834. Accordingly,
Z=Fuzzy_Network_Intersection(X,Y,W)+(X−Y)+(Y−X)

That is, fuzzy network unions of two network subsets may be defined as the sum of the differences of the two network subsets (the nodes and relationships that are uniquely in X and Y, respectively) and the fuzzy network intersection of the two network subsets. The resulting network subset of the difference operator contains any unique relationships between nodes uniquely in an originating network subset and the fuzzy network intersection of the two subsets. These relationships are then added to the fuzzy network intersection along with all the unique nodes of each originating network subset, and all the relationships among the unique nodes, to complete the resulting fuzzy network subset.

For the adaptiverecombinant system800, theresolution function834 that applies to operations that combine multiple networks may incorporate usage behavioral inferences related to one or all of the networks. Theresolution function834 may be instantiated directly by the adaptive recommendations function240 (FIG. 16), or theresolution function834 may be a separate function that invokes the adaptive recommendations function. The resulting relationships in the combined network will therefore be those that are inferred by the system to reflect the collective usage histories and preference inferences of the predecessor networks.

For example, where one of the predecessor networks was used by larger numbers of individuals, or by individuals that members of communities or affinity groups that are inferred to be best informed on the subject of the associated content, then theresolution function834 may choose to preferentially weight the relationships of that predecessor network higher versus the other predecessor networks. Theresolution function834 may use any or all of the usage behaviors270, along with associated user segmentations and affinities obtained during usage behavior pre-processing204 (seeFIG. 9C), as illustrated inFIG. 6 and Table 1, and combinations thereof, to determine the appropriate resolution of common relationships and relationship values among two or more networks that are combined into a new network.

Theobject evaluation function830 may applied when the adaptiverecombinant system800 ofFIG. 16 is used to combine networks. Combining networks requires a determination of which objects212 in two or more networks are identical, or near enough to being identical to be considered identical, for the purposes of combining the networks. In some embodiments, theobject evaluation function830 may enable a global identification management process in which eachobject212 has a unique system designator, which enables direct determination of identity of the objects. This approach may be augmented by the tracking versions or generations ofobjects212, such that the adaptiverecombinant system800 has options for using more recent versions of anobject212 when networks are combined. In other embodiments, theobject evaluation function830 may compare the intrinsic information associated with twoobjects212 to determine whether they are identical or nearly identical enough to be considered identical for the purposes of combining the networks. For example, for text-basedobjects212, associated meta-information234 orinformation232 may be compared between two objects using text-based pattern matching or statistical algorithms. For audio or video-basedobjects212, other appropriate pattern matching algorithms may be applied by theobject evaluation function830 to the associated meta-information234 orinformation232

Fuzzy Process Networks

In some embodiments, implementation of a fuzzy network-based process may be through connecting an existing or new process with afuzzy network500A, as is shown inFIG. 19A. For example, anactivity45 within a process or sub-process136 may precede anotheractivity50 in the sub-process, with anexplicit workflow55 between the activities. It should be understood that there may be a greater number of activities in the process or sub-process136 than the minimal number illustrated inFIG. 19A. Thefuzzy content network500A, managed by the adaptive computer-basedapplication925, which is “external” to the

activities

45,50 in the sub-process136, may be accessible56,57 by one or more of the

activities

45,50.

In other embodiments, implementation of a fuzzy network-based process may be through including an existing or new process within afuzzy network500B managed by the adaptive computer-basedapplication925, as is shown inFIG. 19B. For example, anactivity65 within a process or sub-process137 may precede anotheractivity70 in the sub-process, with anexplicit workflow75 between theactivities75. These activities and their relationships are represented directly within thefuzzy network500B in this case. It should be understood that there may be a greater number of activities in the process/sub-process137 than the minimal number illustrated inFIG. 19B.

In some embodiments, adaptive recombinant processes may employ structures based on fuzzy content networks, as defined in U.S. Pat. No. 6,795,826, entitled “Fuzzy Content Network Management and Access.” These structures may include the use or adaptation of fuzzy content networks and associated topic objects and content objects, as defined therein.

For “inclusive” fuzzy network embodiments, such as thefuzzy content network500B ofFIG. 19B, according to some embodiments,FIG. 20A depicts the structure of a process topic object445t, which consists of meta-information450tonly, and is analogous to a fuzzy content network topic object. Likewise,FIG. 20B depicts aprocess content object445c, which consist of embedded information, or references (for example, pointers or URLs) toinformation455c, and the associated meta-information450c. Fuzzy process content objects455care analogous to fuzzy content network content objects. According to some embodiments, process activities may be included within the fuzzy content network, and as shown inFIG. 21A, and a process activity object445acontains meta-information450a, analogous to the process topic object455tofFIG. 20A. In other embodiments, as shown inFIG. 21B, process activities may be included within the fuzzy content network, and a process activity object446awill contain meta-information451a, as well as information or a pointer toinformation456a, analogous to theprocess content object445cofFIG. 20B. For all of these fuzzy network object structures, relationships and associated relationship indicators may be established between any two process objects in the process network, and there may be plurality of types of relationships and associated relationship indicators between any two process objects. In some embodiments, at least one relationship type denotes process sequence or workflow, and is typically applied among process activity objects, but may apply among other process objects as well.

As reviewed previously,FIGS. 20A, 20B,21A and21B depict in some embodiments how fuzzy network objects may be converted to process network objects, and how special process objects, process activity objects445aand446amay be defined.

FIG. 22A illustrates a process activity “network A”460, including four activities (465a,465b,465c, and465d) and work flow relationships among the activities (470a,470b,470c, and470d), as well as relationships to activities external to process activity “network A”470e. Each relationship has an associatedrelationship indicator471. In some embodiments, the relationship indicator is represented in the form:
Sequence(Relationship type,First Activity,Second Activity)
The relationship indicator “S(1,1,2)”470 ofrelationship470athus implies a relationship oftype1 betweenactivity1 andactivity2, in that sequence.

FIG. 22B illustrates aprocess activity network475, which may have

multiple relationship types

476aand476boutbound from an activity (activity1474a), and may also have multiple relationship types inbound476band476cto an activity (activity4474b). Furthermore, multiple relations of different relationship types may be outbound from one or more activities in the process activity network to destinations outside the process activity network. For example, inFIG. 22B,relationship476dof relationship type2 (S(2,4,M)) is outbound fromactivity4474b; likewise,relationship476ehaving relationship type1 (S(1,4,N)) is also outbound fromactivity4474b.

According to some embodiments,FIGS. 23A and 23B depict

process networks

480A and480B (collectively, process network480). The process networks480A and480B are depicted for a particular relationship and associated relationship indicators, at particular times (t₀and t₂), in some embodiments. The process networks480A and480B are process activity networks (seeFIGS. 22A and 22B). The process networks480A and480B are integrated with process content objects, for example, “content object1”485aand process topic objects, for example, “topic object1”485b. Relationships and associated relationship indicators may exist between process activity objects and process content or topic objects, for example,490.

FIG. 24 is a flow diagram illustrating how process usage information associated with the

process networks

480A and480B are processed, according to some embodiments, over a period of time. During time t₁,usage behavior information920 is tracked and processed (block4495). The adaptive recommendations function240 of theadaptive system100 is invoked (block4500), and the process structure of theprocess network480A is automatically or semi-automatically updated (block4505), resulting inprocess network480B at time t₂. Thus,process network480A at time t₀(FIG. 23A) automatically or semi-automatically becomesprocess network480B at time t₂(FIG. 23B), using the procedure inFIG. 24. Structures that may be updated within the process network480 include relationship indicators; for example,relationship indicators515 betweencontent object1485aandactivity1520 had values of 0.4 and 0.6 at time t₀(FIG. 23A); at time t₂, therelationship indicators515 have values of 0.8 and 0.6 (FIG. 23B). Relationships may be deleted, as for example betweenprocess activity1520, andprocess activity4525 (formerly S(2,1,2) inFIG. 23A). Relationships and associated relationship indicators may be added, as for example530 betweenactivity4525 andcontent object4540. And process objects, and associated relationships may be deleted. For example theformer content object5 ofFIG. 23A and its associated relationships and relationship indicators, is not part ofprocess network480B.

FIG. 25 depictsprocess network480B (FIG. 24B) at time t₂. Process activity objects (shaded) are selected, along with the associated relationships between these process activity objects, as well as other selected process objects that have a relationship to the selected process activity objects, and the associated relationships. In some embodiments, the selection of the process network subset may be through application of network neighborhood metrics, such as degrees of separation metrics, or fuzzy degrees of separation network neighborhood metrics. In other embodiments, other selection methods may be used, including individually specifying process objects and associated relationships. In this example, the result of the selection/sub-setting555 ofprocess network480B isprocess network560.

Adaptive Recombinant Processes

FIGS. 26 and 27 illustrate the syndication and combination of process networks by the adaptiverecombinant system800C. (The process network activity objects are shaded, to distinguish from the content and topic objects.) InFIG. 26, process network subset B560 (FIG. 25) is syndicated to an existingprocess network C580 that may exist on the same computer system or a different computer system. It should be noted that a process network need not represent a “complete” or “functional” process. For example,process network C580 contains two process activity objects581,582 that do not have a direct relationship to one another. In addition, associated

relationships

581rand582rhave no corresponding forward sequence process activity object within theprocess network580. In general, a process network may be fragmentary, without completeness of process objects and relationships.

FIG. 27 illustrates the results of the combination ofprocess network B560 andprocess network C580 by the adaptiverecombinant system800C, and the application of the fuzzy network operators function820, the adaptive recommendations function240 and the object evaluation function830 (FIG. 17). The result isprocess network D590. Note that all distinct process activity objects from560 and580 reside in590, and the associated relationships among the process activity objects are resolved and established. Note also that these relationships may be reflexive, as in the case of591 and592. In the process network subset C580 (FIG. 26), a relationship indicator “S(2,M,4)” is indicated, although no “activity4” is present in thesub-network580. Once syndication with processnetwork subset B560, which includes “Activity4,” occurs, the adaptiverecombinant system800C automatically relates the twoactivities4 and M, as shown inFIG. 27. Other process objects and corresponding relationships may be resolved as previously described.

FIG. 28 illustrates that theprocess network560 may be encompassed by thestructural aspect210C ofadaptive system100C (FIG. 7). Theprocess network560 may be the sole content network withinstructural aspect210C, or may be one of multiple network or non-network structures within210C, as is more generally depicted inFIG. 15, above.

Likewise,FIG. 29 illustrates that theprocess network560 may be encompassed by thestructural aspect210C of theadaptive system100C, which may form part of the adaptiverecombinant system800C. Again, theprocess network560 may be the sole content network withinstructural aspect210C, or may be one of multiple networks within210C, and may be syndicated, modified, and combined with other content or process networks, as is more generally depicted inFIGS. 47 and 48, below. Theprocess network560 or another process network structure within thestructural aspect210C may correspond to theadaptive process instance930 ofFIGS. 4A and 4B, and henceFIGS. 15, 29,47 and48 illustrate the ability to syndicate and combine representations ofadaptive process instances930, thereby enabling the adaptiverecombinant process901.

FIGS. 30A, 30B,31A, and31B illustrate the general approaches associated with process network syndication and combinations, as managed by the adaptiverecombinant system800C, and applied as part of a particular type of application of the adaptiverecombinant process901, designated inFIGS. 30A, 30B,31A and31B asprocess application type901A.FIG. 30A illustrates a hypothetical starting condition, and depicts three organizations,650,655,660. These may be organizations (which may be individuals) within the same business or institution, or one or more may be in businesses or institutions external to the others. A first process network, “process network1”665, is used solely by, or resides within, “organization1”650. A second process network, “process network2”670, is used solely by, or resides within, “organization2”655. “Organization3”660 does not have a process network initially, in this example.

FIG. 30B illustrates that a subset of “process network1”665 is selected to form “process network1A”680. “Process network1A”680 is then syndicated as “process network1A”685 to “organization2.” “Organization2”655 then syndicates “process network1A”685 to “organization3”660 as “process network1A”690. Thus,FIG. 30B illustrates how process networks, or subsets of process networks, can be syndicated among organizations without limit.

FIG. 31A depicts a subset of “process network1”665 and “process network1A”695 residing in “organization1,” in which “process network1a”695 is syndicated to “organization2”655 as “process network1A”700. “Process network1A”700 and the existing “process network2”670 in “organization2” are combined710 to form “process network2a”715 inorganization2655. “Process network2a”715 is then syndicated to “organization3”660 asprocess network2A720.

FIG. 31B represents a continuation ofFIG. 31A, in which additional combination and syndication takes place. “Process network2a”720 in “organization3”660 is syndicated to “organization1”650 asprocess network2A730.Process network2A730 is then combined with the original “process network1”665 in “organization1”650 to generate “process network3”740 in “organization1”650.

FIGS. 30A, 30B,31A, and31B demonstrate that, in some embodiments, adaptive recombinant processes may indefinitely enable sub-setting of process networks, syndicating the subsets to one or more destinations, and enabling the syndicated process networks to be combined with one or more process networks at the destinations. At each combination step, the relationship resolution function834 (of thefuzzy network operators820—seeFIG. 18) and the adaptive recommendations function240 may be invoked to create and update process structure (and content) as appropriate.

According to some embodiments,FIG. 32 depicts possible deployments of process networks within and across organizations or business enterprises. InFIG. 32, two

enterprises

1810,1815 are depicted, but it should be understood the following described process and process network topologies can apply to any plurality of organizations, individuals, or business enterprises. One topology is represented by “Process1”1811 containing one process network,1812, within one enterprise,1810. In another topology, aprocess1816 contains a plurality of

process networks

1817,1818 within one business enterprise,1815. In another topology, aprocess1820 may extend across more than one

enterprise

1810 and1815, and may contain a plurality of

process networks

1821,1822, and1823. Aprocess network1823 may extend across

business enterprises

1810 and1815. Process networks may have common subsets, as exemplified by1822 and1823. Processes and process networks may extend across an unlimited number of organizations or business enterprises as depicted byprocess1830 andprocess network1832.

According to some embodiments,FIG. 33 depicts a process network topology in which aprocess network1840 includes multiple processes, each process contained partially or as a whole within theprocess network1840, and include a multiplicity of other process networks, each process contained partially or as a whole, where each contained process or process network may span a plurality of organizations or business enterprises.

Process Lifecycle Framework

In some embodiments, as shown inFIG. 34, aprocess lifecycle framework3000 may be used as an implementation framework for migrating to adaptive processes, based on the implementation of adaptive recombinant processes, or other methods and technologies.

Theprocess lifecycle framework3000 has two primary dimensions. The horizontal dimension denotes how theorganizing topology3010 of a process is managed—either in a centralized3011 or decentralized3012 manner. The vertical dimension relates to thelocal differentiation3020 of a process—how differentiated3021 or customized3022 the process is for local applications or implementations. The process may be standardized across alllocal applications3021, or customizable tolocal applications3022. The intersections of these dimensions denote fundamental process lifecycle positions. For example, a centralized organizing topology, coupled with standardization of processes across local applications, may be called a “cost and control”quadrant3030. The focus in this quadrant is typically to ensure low cost processes that enforce broad standards across organization and application areas. This is the typical architecture of prior art processes supported by Enterprise Resource Planning (ERP) software that are implemented on a truly enterprise basis.

A decentralized organizing topology, coupled with standardization of processes across local applications, may be called the “ad hoc”quadrant3040. The focus in this quadrant is to enforce broad standards across organization and application areas, but through a decentralized process management and infrastructure approach. This quadrant often represents an inconsistency of objectives, and may be the result of organizational combinations, such as through a merger or acquisition. It is often desirable to not remain in this quadrant in the long-term, as ad hoc implementation typically generates more costs to deliver the same results as the “cost and control”quadrant3030.

A decentralized organizing topology, coupled with customization of processes across local applications, may be called the “Niche Advantages”quadrant3050. The emphasis of this quadrant is to maximize the value of the process in specific application areas through a decentralized process management and infrastructure approach that enables maximum flexibility and tailoring to local needs. This quadrant represents a potentially high value, but also high cost approach. It is often consistent with the development of new processes that provide competitive advantages, where the generation of value from the processes overrides inefficiencies stemming from decentralized process management and heterogeneous enabling infrastructure. Over time, however, as competitive advantages potentially dissipate, the cost penalty associated with this quadrant may be too high compared to the derived benefits.

A centralized organizing topology, coupled with customization of processes across local applications, may be called the “Adaptive Processes”quadrant3060. The emphasis of this quadrant is to maximize the value of the process in specific application areas, but through an efficient, centralized process management and infrastructure approach that enables maximum flexibility and tailoring to local needs. This quadrant represents a potentially high value and low cost approach, and provides advantages versus the other three quadrants. An adaptive process approach has been very difficult to achieve with prior art process and supporting process infrastructure and systems. Theadaptive processes quadrant3060 is the quadrant, in particular, that adaptive recombinant processes advantageously addresses.

According to some embodiments,FIG. 35 is aframework3100 that describes how processes typically include multiple functionality layers3110. For example, these layers may comprise information technology layers, with the highest level corresponding to process work flow and business logic, and lower layers corresponding to more generalized information technology, such as content management, database management systems, and communications networks.

In a process implementation, then, different layers may have different process lifecycle quadrants. For example, the top-most layer may be aniche advantage quadrant3120, the directly supporting layer may be anadaptive processes quadrant3130, and the directly supporting layer of that layer may be a cost andcontrol quadrant3140. In general, it is good practice that the lower process layers should be at least as standardized as the layers above.

According to some embodiments,FIG. 36 represents a processlifecycle management framework3200 that may be advantageously used by businesses and institutions to ensure the highest possible value from their processes over time. Theframework3200 may be understood to represent one specific process lifecycle functionality layer.

Business innovations

3210 may be the source of processes (or process functionality layers) in the Niche Advantages quadrant.Business combinations3230 may be the source of processes in the Ad Hoc Implementation quadrants. It is usually advantageous to migrate from the Ad Hoc Implementation quadrant to the Cost and Control quadrant through more effective leverage ofscale3240. It may be advantageous to migrate from the Niche Advantages quadrant to the Adaptive Processes quadrant through leverage ofmass customization techniques3220. It may also be advantageous to migrate from the Cost and Control quadrant to the Adaptive Processes quadrant through leverage ofmass customization techniques3250. Alternatively, it may also be advantageous to externalize theprocess3260 from the Cost and Control quadrant, where external sources can provide process advantages, typically either through cost effectiveness, or through more effective customization or adaptation to local applications and the same cost.

Adaptive Process Application Areas

Recall fromFIGS. 3, 4A,4B, and4C that adaptive recombinant processes may be applied to improve the functionality of anyprocess168 by integrating adaptive recommendations functions into theprocess168 and applying the adaptive recommendations to facilitate the more effective use of theprocess instance930. The application of the adaptive recommendations may be through delivery ofadaptive recommendations910 to processparticipants200 or by applying the adaptive recommendations to modify thestructure905 and/orcontent935 of computer-basedapplications175 supporting the process, or both.

The following pages include descriptions of severaladaptive processes900 and adaptiverecombinant processes901. Table 3 lists embodiments of theadaptive process900, including an associated figure and claim.

TABLE 3


Adaptive Process Embodiments

Embodiment	Figure	Claim

Adaptive process
900		Claim 1
Adaptiveasset managementprocess 900A		Claim	8
Adaptive real-time learning process 900B		Claim 25
Innovation network process 900C		Claim 34
Adaptive publishing process 900D		Claim 35
Adaptive commerce process 900E		Claim 27
Adaptiveprice discovery process 900F		Claim 28
Adaptivecommercial solutions process 900G		Claim 29
Location-aware collectivelyadaptive process 900H		Claim 37

Likewise, Table 4 lists embodiments of the adaptive recombinant process, including an associated figure and claim.

TABLE 4


Adaptive Recombinant Process Embodiments

Embodiment	Figure	Claim

Adaptiverecombinant process 901		Claim 22
Recombinantprocess network process 901A	FIGS. 30A-B	Claim 23
Adaptiveviral marketing process 901B	FIGS. 49A-B	Claim 31
Evolvable process 901E		Claim 24

Tables 3 and 4 are provided for convenience in understanding the following passages, and are not meant as an exhaustive presentation of the possible applications of theadaptive process900 or the adaptiverecombinant process901. Further, the cited figures and claims are not exhaustive, but are meant as a guide to assist in understanding the following exemplary embodiments.

FIGS. 37-43 depict specific applications of the adaptive process900 (processes900A-900H) or adaptive recombinant process901 (

processes

901A,901B,901E). In some of these applications, theadaptive process900 will include an adaptive system100 (FIG. 7), in which the adaptive system may include some non-adaptive elements (FIG. 8), a fuzzy network structure (FIG. 14), a combination of network and non-network-based structure (FIG. 15), or a process network structure (FIG. 28). Further, the adaptiverecombinant process901 in some of these applications may include an adaptive recombinant system800 (FIG. 16), which may include a fuzzy network structure (FIG. 17), or a process network structure (FIG. 29).

The following illustrations are specific process application areas for which theadaptive process900 or adaptiverecombinant process901 may be advantageously applied, although it should be understood that these application areas do not constitute all the possible applications of theadaptive process900 or adaptiverecombinant process901.

Adaptive Asset Management

According to some embodiments, theadaptive process900 may be used to establish online asset management systems and processes. An on-line asset is defined as any item of software or content, or any tangible or intangible asset that the software or on-line content represents. In other words, the asset to be managed may also be derivative from the representations of the software or content ofadaptive process900.

Recall fromFIGS. 4A and 4B that the adaptive computer-basedapplication925 may integrate with existing and/or newonline computer applications175 to enable capture and analysis ofusage behavior information920. This information may then be used to determine the value of the online computer and software assets. This determination of value of online assets can then be applied beneficially to facilitate asset management processes associated with the on-line assets, optionally including applying a function to automatically or semi-automatically modify the one ormore computer applications175 in alignment with the inferred value of the online assets ofcomputer applications175 to processparticipants200.

FIG. 37 depicts anadaptive process900A, including an adaptiveasset management system1500. Theasset management system1500 includes the adaptive computer-basedapplication925 and anasset management function1510. Although inFIG. 37, theasset management function1510 is shown to be external to the adaptive computer-basedapplication925, it should be understood that theasset management function1510 may be configured to be internal to the adaptive computer-basedapplication925. Further, although not shown inFIG. 37, the adaptive computer-basedapplication925 may contain theadaptive system100.

Theasset management function1510 receivesinformation1520 associated with data regarding theusage behaviors920 ofprocess participants200, or inferences of the preferences and interests of online assets associated with the processparticipant usage behaviors920. Theasset management function1510 uses theinformation1520 to derive the value of online assets. The derived value may be of different magnitudes for different individuals or communities ofprocess participants200. The asset valuation information determined by theasset management function1510 may be applied to decide near-term or long-term online asset changes and directions. For example, a high-value on-line asset might be made more prominently available forprocess participants200, while less valuable assets might be made less prominent, or eliminated from the content andcomputer applications175. New development projects to deliver on-line assets that are expected to be of high value based on the valuations of theasset management function1510 may be conducted. Further, in addition to on-line assets, features associated with the assets may be evaluated by theasset management function1510, and appropriate asset modifications or development projects initiated. For some modifications, theasset management function1510 may be used to support making the appropriate changes.

Theasset management function1510 may automatically or semi-automatically modify1505 the adaptive computer-basedapplication925. For alternative embodiments in which theasset management function1510 is internal to the adaptive computer-basedapplication925, the adaptive self-modification operation1505 is analogous to thestructural modifications905 of theadaptive system100, the adaptiverecombinant system800, and the generalized adaptive computer-basedapplication925, described above. Likewise, theasset management function1510 may automatically or semi-automatically modify1515 content within adaptive computer-basedapplication925. For embodiments in which theasset management function1510 is internal to the adaptive computer-basedapplication925, the adaptive self-modification ofcontent1515 is analogous to the content-based

modifications

935,905 of the

aforementioned systems

100,800,925 (represented in parentheses). Further, other computer applications andcontent175 may be automatically or semi-automatically modified1525 by theasset management function1510 in accordance with valuations derived byasset management function1510. In such cases, even if direct usagebehavioral information920 are not available fornon-adaptive computer application181 andcontent180, theasset management function1510 may make inferences based on analogy from interactions of theprocess participants200 with the adaptive computer-basedapplication925 to generate appropriate valuations.

Note thatadaptive recommendations910 delivered to processparticipants200 is not an essential feature for enablingprocess application900A.

Adaptive Real-Time Learning

Theadaptive process900 may be used to establish an adaptive process environment930 (FIGS. 4A and 4B) to promote enhanced learning byprocess participants200, including real-time learning, for existing or new processes through the implementation ofadaptive recommendations910 that are delivered directly to the process participant oruser200, or indirectly through adaptive modification of theprocess network structure905 orcontent935. In some embodiments, the resulting environment may be metaphorically termed an adaptive online “cockpit” of process knowledge and activities that effectively “surrounds” the process user. This approach facilitates the real-time learning ofprocess participants200, rather than relying solely or primarily on classroom or other episodic forms of education or training.

FIG. 38 illustrates anadaptive process900B, or adaptive real-time learning process, including an exemplaryprocess participant interface1600 associated with acomputing device964 that is interacted with byprocess participants200. It should be understood that althoughFIG. 38 illustrates a visual, display-oriented process participant interface, the interface could be audio-based, tactile or kinesthetically-based, or the interface could be comprised of combinations of visual, audio, or kinesthetic elements. Theprocess participant interface1600 of theadaptive process900B may include one or more instances of displayedadaptive recommendations910 associated with the adaptive computer-basedapplication925, in which theadaptive recommendations910 are formatted for viewing in a specified manner. InFIG. 38, a first formattedinstance1610 and a second formattedinstance1620 ofadaptive recommendations910 are shown. Theprocess participant interface1600 may containother information915 derived from the adaptive computer-basedapplication925, formatted as appropriate for display. A formattedinstance1630 ofinformation915 from the adaptive computer-basedapplication925 is shown. A formattedinstance1630 may contain one or more instances ofadaptive information1632 and/ornon-adaptive information1634. Recall fromFIG. 4A thatadaptive information1632 is content, structural elements, objects, information, or computer software that has been adaptively self-modified905,935 by the adaptive computer-basedapplication925 based, at least in part, onusage behaviors920 ofprocess participants200.Non-adaptive information1634 denotes any other information, content, objects, or computer software encompassed by the adaptive computer-basedapplication925 that has not been adaptively self-modified905,935.

Theprocess participant interface1600 may also contain formattedinstances1640 of other information such as information derived fromother content180aandother computer applications181athat are relevant to processparticipants200.

Formatted

instances

1610,1620 ofadaptive recommendations910 and formatted instances of adaptivecomputer application information915 may contain explicit educational or training information or content, or relevant references or “help” information, in addition to more general information or content relevant to the associated process. In some embodiments, the adaptive computer-basedapplication925 may include or interact with a learning management system that may provide guidance on the appropriate educational or training information to include in theadaptive recommendations910.

Innovation Networks

According to some embodiments, theadaptive process900 may be used to create adaptive “innovation networks” that may be applied to facilitate collaborative research and development processes. These processes may be applied within an organization, or span an unlimited number of organizations or individuals. In some embodiments, adaptive recombinant processes may utilize the systems and methods of PCT Patent Application No. PCT/US05/001348, entitled “Generative Investment Process,” filed on Jan. 18, 2005, which is hereby incorporated by reference as if set forth in its entirety, to enable innovation networks and processes.

FIG. 39 illustrates anadaptive process900C, or innovation network process, including the adaptive computer-basedapplication925, which includes theadaptive system100. Thestructural aspect210 of theadaptive system100 encompasses aninnovation map1700, which associatesopportunities1710 tocapability components1730, shown inFIG. 39 organized within capability component categories ortypes1720. Opportunities, capability component types, and capability components may be collectively termed the “elements” ofinnovation map1700. It should be understood that although theinnovation map1700 is depicted inFIG. 39 in a table format, theinnovation map1700 may be organized in network structure, including a fuzzy network structure. Further, theinnovation map1700 may be incorporated within a process network, such as inFIG. 25 (not explicitly shown inFIG. 39) within thestructural aspect210.

“Opportunities,” as defined herein are ideas that can potentially generate value and that involve investments of time, resources, or financial commitments. These opportunities may be within defined processes, such as business development and growth processes, commercial venture capital, corporate venturing processes, business incubation processes, marketing processes, research and development processes, and innovation processes, or the investment processes and associated activities may be more ad hoc in nature.

Typically,opportunities1710 consist of a bundle of two or more capability components, such as “cc5” and “cc7”1730. For example, even if a business idea (opportunity) is based on a technological break-through, the overall business venture idea is likely to also include other differentiating components, such as processes (e.g., marketing processes). It is the uniqueness of the bundle of components that typically provides the economic value-creating potential of the idea.

Capability components

1730 may include both tangible and intangible aspects of anopportunity1710. Thecapability components1730 may constitute a mutually exclusive, collectively exhaustive set for eachopportunity1710. (The term collectively exhaustive, as used herein, means that the elements of a set comprise the totality of the set.) Or, thecapability components1730 may represent just a subset of theopportunity1710 defined and may simultaneously be represented inmultiple opportunities1710. A myriad of possibilities exist for representingopportunities1710 usingcapability components1730.

Thecapability components1730 of theinnovation map1700 are individual instances of capability component categories ortypes1720.Capability types1720 may include, but are not limited to, products (including prototypes), technologies, services, skills, relationships, brands, mindshare, methods, processes, financial capital and assets, intellectual capital, intellectual property, physical assets, compositions of matter, life forms, physical locations, and individual or collections of people.

The objective of any innovation process is to maximize the volume ofhigh value opportunities1710 generated at the lowest possible cost. Meeting this objective is a function of multiple variables. One variable is the volume, breadth and quality of thecapability components1730. Another variable is the ability to combine capability components in a large variety and novel ways. A third variable is the degree to which the greatest diversity of human attention to be applied, and applied in the right places. Theadaptive process900C can be used to enable processes that beneficially affect these key variables of innovation process success.

The adaptive computer-basedapplication925, together with theinnovation map1700, enables more effective innovation-based processes in several ways. First, elements of theinnovation map1700 may includeadaptive recommendations250 that are delivered to processparticipants200. This approach can help makeprocess participants200 aware of particularly relevant elements of theinnovation map170. Second, the adaptive recommendations function240 may be applied to modify905 theinnovation map1700 based on, at least in part, inferences onprocess participant200 preferences or interests. This can facilitate the efficient development and maintenance of a collective innovation map that can most beneficially serve the interests of theprocess participants200, including maximizing the number of high value opportunities generated withininnovation map1700. Third, elements of theinnovation map1700 may be syndicated, modified, and recombined amongprocess participants200 through the application of the adaptiverecombinant system800, enabling multiple, distributed innovation map instances. This structure can facilitate both shared and private innovation maps, effectively balancing the advantages of economies of scale and local interests. The adaptive recombinant system approaches ofFIGS. 47, 48,49A, and49B may be applied to the syndication, modification, and recombination of elements ofinnovation map1700.

The adaptive computer-basedapplication925 may contain, or interact with, auxiliary functions (not shown inFIG. 39) that may additionally facilitate innovation processes. For example, the adaptive computer-basedapplication925 may contain functions to enable automatic or semi-automatic evaluation ofopportunities1710, to automatically or semi-automatically generateadditional opportunities1710 through combinatorial operations oncapability components1730, and/or to facilitate effective information gathering or experimental design associated with uncertainties with regard tocapability components1730 or other elements ofinnovation map1700. These additional functions may all be managed within an adaptive process network, such as the adaptive process network ofFIG. 25 within thestructural aspect210 of theadaptive system100.

Adaptive Publishing

Theadaptive process900 may be applied to enable adaptive publishing systems and processes. Theadaptive process900, when applied to enable adaptive publishing systems and processes, may generate adaptive analogs to non-adaptive “broadcasted” media such as print publications, radio programs, music albums or soundtracks, television programs, films, or interactive games; as well as generating adaptive media that may not have specific broadcast analogs. In some embodiments, the methods and systems defined by U.S. patent application Ser. No. 10/715,174, entitled “A Method and System for Customized Print Publication and Management,” may be integrated with adaptive recombinant processes to enable an adaptive publishing process.

FIG. 40 depicts anadaptive process900D, or adaptive publishing process, according to some embodiments. Anadaptive publishing function2000 that is included within the adaptive computer-based application925 (although in other embodiments, theadaptive publishing function2000 may be external to the adaptive computer-based application925) receives input from theadaptive system100. The input may be in the form ofadaptive recommendations940 suitable for the adaptive publishing purposes, generated fromadaptive recommendations250, or the input may be in the form ofinformational content2031 contained in thecontent aspect230 of theadaptive system100. The content originating from thecontent aspect230 may have been modified935 by the adaptive recommendations function240. In either case, theadaptive publishing function2000 uses the inputs from theadaptive system100 to generate media that is appropriately customized for the recipients of the

media

200,260. This customization of an adaptive publication, or media instance, may include the specific elements of content that will be contained in a media instance, and also the arrangement of the elements of content in the media instance. Thus, a media instance, as used herein, is a distinct set of objects or information in combination with a unique arrangement of the objects or information. The customization of media into specific media instances is performed on the basis of

inferred media recipient

200,260 preferences and interests, which are in turn based on recipient interactions with theadaptive system100, or through inferred affinities between the media instance recipient and other individuals that have interacted withadaptive system100.

As shown inFIG. 40, theadaptive publishing function2000 generates one or more instances ofmedia2030, adapted appropriately to the preferences or interests of the

media recipients

200,260. Each media instance contains one or more elements of content, some or all of which may beobjects212 or information232 (FIG. 9A) contained in theadaptive system100. Although not shown explicitly inFIG. 40, a media instance may also explicitly or implicitly include relationships amongobjects214 associated with thestructural aspect210 of theadaptive system100.

As shown in the example ofFIG. 40,media instance2010 contains multiple objects in a particular configuration, including “Object A”2012 and “Object D”2014. Recall that theobjects212 of theadaptive system100 may contain any form of digital information, including text, graphics, audio, video, and executable software. These objects may be transformed to alternative media forms by theadaptive publishing function2000. An individual media instance can therefore be defined as a set of information objects212 orinformation items232 and a particular arrangement of the objects of information items. So, as one example, on-linetextual objects212 may be transformed into printed media by theadaptive publishing function2000. In the case of printed media, a specific media instance is determined by not only the objects to be included in a media instance, but also the arrangement or print layout of theobjects212 and any other content included within the media instance. The information objects212 within a media instance may be substantive in nature, or non-substantive (e.g., promotional or advertising information).

In accordance with inferred preferences and interests of the intended recipients,media instance2020 contains a different set of objects and a different arrangement of objects thanmedia instance2010. For example, “Object A”2012 exists in both

media instance

2010 and2020, but for example, “Object D”2014 is unique tomedia instance2010 and “Object E”2024 is unique tomedia instance2020.

Although theadaptive media instances2030 ofFIG. 40 depict differing arrangements of objects and other items of content in accordance with a spatial orientation, consistent with, for example, physical spatially-oriented media such as printed media, including newsletters, newspapers, magazines, and books, it should be understood that the customized object selection and arrangement of theadaptive publishing function2000 may apply to other media types as well. In such cases, the arrangement of elements of the media instance may be other than spatial in nature; for example, the arrangement may be temporal-based for media containing information than is typically “consumed” sequentially. For example, foraudio objects212 orinformation232 such as songs, the specific songs selected, and arrangement of the songs in a sound track may be different across media instances. For video ormulti-media objects212 orinformation232, customized media instances may include applying theadaptive publishing function2000 to choose different musical sound tracks for corresponding elements of video, or even generating different media instances containing different elements of, or a different sequence of, the plot or story line of the video. For interactive media, such as computer-based games, the game instance may be customized by theadaptive publishing function2000 through the selection of different software modules of the game, or by arranging the software modules of the game in different ways in different media instances.

For audio and/or video-basedobjects212 orinformation232, theadaptive publishing function2000 may generate media instances that constitute “programs,” which are adaptive analogues of radio programs, television programs or other broadcasted forms.

Media instances may be delivered or otherwise made available2002 to processparticipants200, or made available2004 tonon-process participants260. Media instances may take a purely digital form that can be embodied in a variety of physical forms. They may be available to recipients in the purely digital form, or they may be available to processparticipants200, or tonon-process participants260 through other physical embodiments. A media instance may be printed, for example. A media instance may be stored on portable storage media such as CD-ROMs or DVD's.

Theadaptive publishing function2000 of theadaptive process900D may apply additional logic or information in generatingadaptive media instances2030 that may not be available from the usage aspect220 of theadaptive system100. For example, a record of what objects212 orinformation232 have been contained in media instances received by particular recipients may be used to ensure that a recipient does not receive another media instance that contains information the recipient is likely to have already seen or heard. (This rule might be relaxed or adjusted, for example, for non-substantive content that is included for advertising or promotional purposes.) Theadaptive publishing function2000 may also include special capabilities for managing advertising or promotional information within each media instance. These capabilities seek to optimize or to control advertising or promotional content such that the content will be of the most value to consumers or producers of themedia instances2030, while aligning the frequency and prominence of the advertising or promotional information with the terms and conditions agreed to by suppliers of the advertising or promotional content. The advertising or promotional content may exist within theadaptive system100, or may be managed within theadaptive publishing function2000.

Theadaptive publishing function2000 may apply other global considerations or rules in generating adaptive media instances. For example, limits on the amount of information within a media instance may influence the composition of the media instances. The informational limits may be measured, for example, in terms of the number of words or number of pages for text-based media, or, for example, by duration for audio or video-based media. Furthermore, there may be limits on the number of unique media instances generated, and in this case theadaptive publishing function2000 may apply optimization algorithms to determine media instance composition and arrangement so as to collectively benefit

media recipients

200,260 while conforming to the limits on the number of unique media instances.

Theadaptive publishing function2000 may also apply specific formatting features to media instances. For example, for text-based media instances, specific fonts, font-size, colors, line spacing, and other format variations may be applied in accordance with inferred preferences of

media recipients

200,260. Theadaptive publishing function2000 may also apply alternative languages to media instances in accordance with inferred preferences of

media recipients

200,260.

Although not explicitly shown inFIG. 40, information regarding media instances and the corresponding recipients within theadaptive publishing function2000 may be made available to theadaptive system100, and constitute another behavioral aspect incorporated by the usage aspect220, that can be used by the adaptive recommendations function240 in generating subsequent recommendations.

Adaptive Commerce

Adaptive processes may be employed to recommend products orservices910 based not only oncustomer200 buying behaviors and patterns, but also within the context of auxiliary information or rules that may be specific to the customer orpotential customer200, the customer's organization, and/or the products and services purchased.

According to some embodiments,FIG. 41 depicts anadaptive process900E, or adaptive commerce process, which includes the functions of an adaptive commerce application. Acommerce contextualization function2100 within the adaptive computer-basedapplication925 provides additional contextualization to theadaptive system100 for use by the adaptive recommendations function240. Thecommerce contextualization function2100 may deliver information to theadaptive system100 directly2141 to the adaptive recommendations function240, or throughinformation transfer2140 to the usage aspect220. It should be understood that thecommerce contextualization function2100 may be external to the adaptive computer-basedapplication925, in some embodiments, and transfer information to the adaptive computer-basedapplication925; which may, in turn, transfer the information to theadaptive system100. It should also be understood that although thecommerce contextualization function2100 is shown inFIG. 41 to be external to theadaptive system100, some or all of the functions ofcommerce contextualization function2100 could alternatively be internal to theadaptive system100. For example, some or all of the information associated with thecommerce contextualization function2100 could be directly derived fromprocess participant behaviors920 and stored and processed in usage aspect220.

Thecommerce contextualization function2100 of theadaptive process900E includes one or more functional elements, each of which may include relevant information and procedures or algorithms. As shown inFIG. 41, thecommerce contextualization function2100 may include acustomer context function2110, apurchase history function2120, and a product/service attributes function2130. Thecustomer context function2110 includes contextualization information associated with thecommercial process participants200, or customers, that are not available through inferences fromcustomer behaviors920. For example, for business customers, thecustomer context function2110 may include information regarding office site and layout or other business environment-related information. Such information may prove useful in providingrecommendations910 that are most relevant given the business environment of the customer. As another example, pertaining to recommendations to consumers, thecustomer contextualization function2110 may contain information on family members of a particular customer, including gender, age, etc., thereby enabling tuning of recommendations910 (as one example, in the case of gift recommendations) appropriately.

Thecommerce contextualization function2100 may also, or alternatively, include apurchase history function2120. This function includes a mapping of customers to purchases of products or services over time. This information can be used by the adaptive recommendations function240 to deliver moreeffective recommendations910. For example, purchase patterns that are embedded in the information associated with thepurchase history function2120, combined withusage behaviors920, may enable therecommendation function240 to generateimproved recommendations910 through incorporation of insights associated with purchase timing patterns. For example, it may be determined by application of thepurchase history function2120 that a certain business customer buys certain products only twice a year, and always in conjunction with another product type. Therecommendations910 may then be appropriately aligned with this pattern.

Thecommerce contextualization function2100 may also, or alternatively, include a product or service attributes function2130. This function includes additional information or context for product or services. As an example, for durable products or goods, a schedule of depreciation may be included in the product/service attributes function2130. Such information may enable the adaptive recommendations function240 to tune recommendations to be consistent with the expected lifespan of previously purchased products.

Thecustomer context function2110, thepurchase history function2120, and the product/service attributes function2130 may be applied independently, or collectively, in providing additional information toadaptive system100 to be used by the adaptive recommendations function240.

Adaptive commerce applications may be applied to adaptive price discovery processes, so as to more advantageously determine prices for products or services. Thus, anadaptive process900F, or adaptive price discovery process, is depicted inFIG. 42, according to some embodiments. In addition to thecommerce contextualization function2100, the adaptive computer-basedapplication925 may include, or have access to, aprice discovery function2150 that provides input to the adaptive recommendations function240 of theadaptive system100.

Process participant behaviors

920 may be used to infer conscious or unconscious intensity of desire for a product or service, or a collection of products or services. Based on these inferences, as well as information orrules2151 from theprice discovery function2150, and optionally, information from thecommerce contextualization function2100, the adaptive recommendations function240 generatesadaptive recommendations910 that include prices for products or services that, in some embodiments, are optimized to yield the highest price that is expected to achieve a sale of the associated product or service to theprocess participant200. In other words, the price may be set at a level that is expected to maximize the seller's capture of the buyer's economic rent. The process participant behaviors and associated inferences may be transferred2152 from the adaptive recommendations function240 to theprice discovery function2150. Other contextual information may be applied by the combination of theprice discovery function2150 and the adaptive recommendations function240 to price appropriately. For example, the price optimization may be adjusted as appropriate based on whether the sales transaction is expected to constitute a one-time relationship, or whether future transactions may take place. The results from the recommendedprices910 may be used to determine inferred price sensitivities andelasticities2155 for one ormore process participants200. Thus, theprice discovery function2150 may supplyuseful information2151 to the adaptive recommendations function240, enabling optimal product pricing; likewise, the adaptive recommendations function240 may supplyuseful information2152 to theprice discovery function2150 for determining prices, price elasticities, or other pricing functions.

Theprice discovery function2150 may include a price discovery experimental design function that is applied to optimize the testing of prices through theadaptive system100. Hence, the combination of theprice discovery function2150 and theadaptive system100 can constitute a “closed” loop adaptive pricing function that applies insights onprocess participant200

behaviors

920 to adjust pricing. In some embodiments, theprice discovery function2150 may apply the methods and systems described in U.S. Provisional Patent Application Ser. No. 60/652,578, entitled “Adaptive Decision Process.”

The adaptiveprice discovery function2150 may employ price discovery and pricing methods other than setting a fixed price for a product or service. For example, thefunction2150 may apply a bidding processes in whichmultiple process participants200 bid on the product or service, or other collective price formation that utilize direct or indirect interactions amongprocess participants200.

The adaptiveprice discovery function2150 may utilize other supplier contextual information to establish prices. This information may be accessed directly from the commerce contextualization function (not shown), or from2152 the adaptive recommendations function240. This information may include the associated inventory level, production cost, production plan, and/or other supply chain considerations that may be relevant in establishing price levels for a product or service.

This adaptive pricing approach of theadaptive process900F may be particularly applicable in setting prices for collections, combinations or “bundles” of products and services that may be specific or even unique to a given customer or set ofcustomers200. The uniqueness of the bundle enables the provision of a maximum value-add to the customer by fine-tuning a recommended “solution” to a perceived customer need that is comprised of multiple products or services. Such a customized solution can increase the value, or economic rent, to the customer. But, the uniqueness of the bundle also decreases the ability of the customer to “comparison shop,” and this reduced transparency enables the supplier to potentially capture a greater portion of the value-add of the customer. Hence, there is an opportunity for the supplier to create more value for customers and to prominently share in the increased value.

FIG. 43 depicts anadaptive process900G, or adaptive commercial solutions process. In addition to featuring theadaptive system100,commerce contextualization function2100, andprice discovery function2150, theadaptive process900G includes a product and/orservice bundling function2160 within the adaptive computer-basedapplication925. (A specific product/service bundle or combination may be termed a “solution.”) The product/service bundling function2160 providesinformation2161ato the adaptive recommendations function240 to enableadaptive recommendations910 to include product/service bundles or solutions to processparticipants200 that are expected to be relevant or compelling to theprocess participants200. Likewise, the adaptive recommendations function240 providesinformation2161bassociated with inferences on the preferences or interests of process participants orcustomers200. The product/service bundling function2160 may be applied in concert, or interact with2162, theprice discovery function2150; together comprising a solution development and pricing process. The adaptive recommendations function240 may combine inputs from the product/service bundling function2160, theprice discovery function2150, and thecommerce contextualization function2100, along with information from the usage aspect220 in generating recommendations that may include solutions and associated pricing.

The product/service bundling function2160 may provide suggested product orservice configurations2161a, in addition to, or instead of, product and service bundle suggestions oroptions2161a. The term “configurations” as used herein in conjunction with the product/service bundling function2160 denotes a set of product or service features. For example, various product components or features may be combined on a customized basis for a specific customer orcustomers200. One example is the customization of the configuration of a personal computer—a specific CPU, with specific storage devices, peripherals, monitor type, etc., may be suggested by the product/service bundling function2160 based oninformation2161bon inferred preferences from the adaptive recommendations function240.

Continuing the example, the suggested customized personal computer may then be bundled by the product/service bundling function2160 with a digital camera and a special warranty that encompasses both the personal computer and the camera. This bundle of products and services may then be specially priced by theprice discovery function2150, with the entire bundle of products and services, the configurations of the products and services, and bundle pricing being informed by the inferred preferences and interests of process participants (customers)200.

The product/service bundling function2160 and adaptiveprice discovery function2150 may be applied together to create a bidding process for product/service bundles. The product/service bundling function2160 may generate bundles or solutions applicable tomultiple process participants200, and the adaptiveprice discovery function2150 is used to organize and manage the bids. The adaptive computer-basedapplication925 may use theadaptive system100 and the product/service bundling function2160 to determine the best mix of bundles and process participants to maximize the value of the auction.

The product/service bundling function2160 and adaptiveprice discovery function2150 may utilize other supplier contextual information to establish solutions and associated prices. This information may be accessed directly from the commerce contextualization function (not explicitly shown inFIG. 43), or indirectly from2152,2161bthe adaptive recommendations function240. This supplier contextual information may include the associated inventory level, production cost, production plan, and/or other supply chain considerations that may be relevant in establishing price levels for one or more products or services, and/or configurations thereof.

Location-Aware Adaptive Sales and Marketing

Recall from Table 1 thatprocess participant behaviors920 may include behaviors associated with physical location, and the movement among physical locations, ofprocess participants200. According to some embodiments, theadaptive process900 may be applied to enable sales or procurement-related processes in which the sales processes of a potential supplier monitor physical locations ofpotential customers200 and deliveradaptive recommendations910 that are appropriately contextualized for the customer's location, or location history. Further, the customers orpotential customers200 may themselves employ systems that interact at varying levels of interaction and cooperation with the supplier's sales processes. Where both the supplier and the potential customers employ adaptive recombinant processes and the potential customer and/or the potential supplier is mobile, a location-aware collectively adaptive system and associated location-aware collectively adaptivecommercial process900H is enabled

Theinformation flow2210 between any two adaptive computer-based application instances of the location-aware collectivelyadaptive system2200 may include the following:

- 1) Polling and detection of a second adaptive computer-based application instance by a first adaptive computer-based application instance.
- 2) Identifying the detected second adaptive computer-based application instance by the first adaptive computer-based application instance.
- 3) Determining a mutual contextual basis for further interaction—that is, either a) from the potentially supplier-side adaptive computer-based application instance, determining whether the potential receiving or customer-side adaptive computer-based application instance encompasses a customer context or set of inferred preferences or interests that would enable one or morerelevant recommendations910 to be generated for theprocess participants200 of the customer-side adaptive computer-based application instance; or b) from the potentially receiving or customer-side adaptive computer-based application instance, determining whether the supplier-side adaptive computer-based application instance encompasses a supplier context and product or service attributes that would enable an expected one or morerelevant recommendations910 to be generated for theprocess participants200 of the customer-side adaptive computer-based application instance. This determination of a mutual contextual basis for further interaction may be made by one or the other, or both instances.
- 4) Receiving from, or supplying to, the second adaptive computer-based application instance contextualized information that enables either a) theadaptive recommendations910 of the first adaptive computer-based application instance to selectively utilize the contextualized information of the second adaptive computer-based application instance; or b) enables theadaptive recommendations910 of the second adaptive computer-based application instance to selectively utilize the contextualized information of the first adaptive computer-based application instance.
  It should be noted that theinteractions2210 may occur between any two adaptive computer-basedapplications925. For example, theinteractions2210 may be between two stationary adaptive computer-based application instances, such as theinformation flow2250 betweeninstance925s1 andinstance925s2. Or theinformation flow2230 may be between two mobile adaptive computer application instances, such asinstance925m1 andinstance925m2. Finally, theinteractions2220 may be between a stationary adaptive computer-basedapplication instance925s1 and a mobile adaptive computer-basedapplication instance925m2.

According to some embodiments,FIGS. 45 and 46 depict two examples of location-aware collectivelyadaptive systems2200.FIG. 45 (2200A) provides additional details regarding the constituent adaptive computer application instances, and the interactions among the instances, of the location-aware collectivelyadaptive system2200 ofFIG. 44. A stationary adaptivecomputer application instance925sincludes anadaptive system100 and a supplier commerce contextualization function2300 (seeFIG. 43). The suppliercommerce contextualization function2300 is comprised of one or more of 1) asupplier context function2310, 2) apurchase history function2120, and 3) a product andservice attribute function2130. Although not shown inFIG. 45, the suppliercommerce contextualization function2300 may also include acustomer context function2110. Thesupplier context function2310 includes contextual information about the potential supplier that is utilizing or applying the adaptive computer-basedapplication instance925s, that is not contained in product and service attributes function2130. For example,supplier context function2310 may include the physical location of the supplier, the hours of business, the history of the business, and any other information that may be relevant to a customer or prospective customer. Theadaptive system100 of the adaptive computer-basedapplication925sinteracts2305 with the suppliercommerce contextualization function2300, as desired, to deliver effectiveadaptive recommendations910sto processparticipants200s.

The stationary adaptive computer-basedapplication instance925sinteracts2415 with the mobile adaptive computer-basedapplication instance925m. The mobile adaptive computer-basedapplication instance925mincludes anadaptive system100 and a mobile customercommerce contextualization function2400. The mobile customercommerce contextualization function2400 includes one or both of a 1)customer context function2110 and 2) a preferences and interests function2420. The preferences and interests function2420 contains inferred preferences and interests ofprocess participants200mbased on their interactions withadaptive system100.

The stationary adaptive computer-basedapplication instance925sinitially interacts2415 with the mobile adaptive computer-basedapplication instance925mthrough an initial detection by one or the other of the instances, or through mutual detection. Next, aninteraction2425 is invoked that seeks to establish a basis for commercial interaction between the two instances. Information from mobile customercommerce contextualization function2400 is compared to information in the suppliercommerce contextualization function2300. So for example, a servicestation employing instance925sdetecting amobile process participant200mthat is a child riding a bicycle is unlikely to have a basis for initiating a commercial interaction, and therefore interactions would cease, whereas if themobile process participant200mwas a truck driver driving a truck that was due for service, then a basis for commercial interaction may exist.

The adaptive computer-based

application instances

925s,925mmay apply location information, or inferences derived from location and time, in establishing a context for commercial interaction or for generation of adaptive recommendations within the location-aware collectivelyadaptive system2200. The adaptive computer-based

application instances

925s,925mmay utilize geographic-related context or information such as through access to digitized maps in making inferences from location and time information associated withprocess participants200.

For example, the respective physical locations of two or more instances may be a determinant of a basis for commercial interaction or for generating adaptive recommendations. The prospective customer or prospective supplier may have thresholds of distance that may be applied to determine a basis for commercial interaction. This threshold distance may be in absolute terms, or in terms of expected transmit time between a mobile adaptive computer-based instance and a stationary instance or another mobile instance. Inferred direction and speed of a mobile instance may be calculated and used as input to establishing context for commercial interaction or for generating adaptive recommendations. Further, the inferred mode of transportation of themobile process participant200 may be a determinant for commercial interaction or generation of recommendations, as such information may affect the expected transmit time or ease of access to the supplier.

Assuming that a basis for commercial interaction is established, a next level ofinteraction2435 may be established between the two

instances

925m,925s. The preferences andinterests2420 of the mobile adaptive computer-basedinstance925mare accessed by the stationary adaptive computer-basedinstance925sto determine if there is a basis for providing suggested products or services to the mobileadaptive computer instance925m. If the suppliercommerce contextualization function2300 determines that there is a basis for suggesting or recommending products, then these are transmitted2445 to mobile adaptivecomputer application instance925m.

FIG. 46 (2200B) illustrates that the mobile adaptive computer-basedapplication instance925m, along with the associatedprocess participants200m, may be considered theprocess participants200smof the stationary adaptive computer-basedapplication instance925s. The interactions described inFIG. 45 are conducted through theprocess participant behaviors920 transmission to theinstance925s, and through theadaptive recommendations910sgenerated byinstance925sand received byprocess participants200sm. Although inFIG. 46, the respective

adaptive application instances

925s,925mare stationary and mobile, respectively, it should be understood that the example may be reversed, or two stationary or two mobile instances may utilize the same topology for interactions, as depicted inFIG. 46.

The location-aware collectivelyadaptive system2200 andprocess900H (FIG. 44) may be applied to a variety of sales and procurement process areas. For example, restaurants can apply such processes by providing prospective diners that are in the vicinity of relevant recommended options, tuned to the prospective diner's particular preferences and tastes.

The location-aware collectivelyadaptive system2200 andprocess900H may further apply the adaptive price discovery systems and processes ofFIG. 42 or the adaptive commercial solutions systems and process ofFIG. 43.

A mobile adaptive computer application instance82bm1 may be embodied within a portable computing device, such as a mobile phone or personal digital assistant (PDA). A mobile adaptive computer application instance82bm1 may be contained in mobile apparatus, such as vehicles or other transportation devices. In some embodiments, a mobile adaptive computer application instance82bm1 may reside within a self-propelled device or appliance.

Adaptive Viral Marketing

In the prior art, viral marketing techniques are known that promote the initial recipients of a sales or marketing-related message to re-send the message to others. For example, viral marketing through e-mail messages is a familiar technique. However, prior art viral marketing techniques exhibit two significant limitations: 1) there is little ability for a recipient to easily modify the received message for the benefit of others he or she will re-send the message to, and 2) the structure of the message is typically a single item of information embodied in a single computer file (such as a e-mail message, or a text document).

According to some embodiments, an application of adaptiverecombinant process901, adaptiverecombinant process901B, may be used to advantageously transform customer relationships, promote sales, facilitate business development, enhance public relations or generally increase “share of mind.” In contrast to the prior art, through the application adaptiverecombinant process901B, content networks or process networks comprising multiple units of interconnected information may be syndicated to potential customers or individuals or institutions for whom influence is sought. The content or process networks may then be syndicated to the customer's customers or influence targets, and so on, potentially without limit. At each stage of syndication and receipt, one or more content or process networks may be modified or combined, optionally enabled by an adaptive recommendations function240. The content within the syndicated content networks may be substantive or non-substantive (e.g., advertising or promotional content). This application of adaptiverecombinant process901B provides a much more powerful and comprehensive approach to viral marketing and public relations than is possible with prior art approaches.

FIG. 47 illustrates an adaptive recombinant systems construct to manage syndication and recombination of network structures for a variety of process purposes, including enabling adaptiveviral marketing process901B. Recall fromFIGS. 16 and 17 that the adaptive recombinant computer-basedapplication925R may include the adaptiverecombinant system800C, which in turn, may encompass theadaptive system100C (FIG. 14). In the embodiment ofFIG. 47, theadaptive system100C manages multiple networks within thestructural aspect210C. These networks may be content networks or process networks, and may be fuzzy networks. For example, some or all of “network1”2510 may be syndicated2515 to “network2”2520 and combined, followed by some or all of the resulting network combination syndicated2525 to “network3”2530 and combined with “network3”2520. A closed loop may be formed, as some or all of this last network combination may be syndicated2535 back to the original “network1”2510 and combined with “network1”2510. This process may continue indefinitely. At each stage, it should be understood that a network may be syndicated to a recipient that does not possess a network. Such a recipient may nevertheless modify the network and re-syndicate. For each stage, the selection, syndication, modification, or combination is enabled by the functions of the adaptiverecombinant system800C, as described previously. Thus, the adaptive recommendations function240 may be applied to facilitate these syndications, modifications, and combinations based, in part, on inferences of preferences and interests from theusage behaviors920 ofprocess participants200.

FIG. 48 illustrates an alternative adaptive recombinant systems construct using an adaptiverecombinant system800ito manage syndication and recombination of network structures for a variety of process purposes, including enabling adaptiveviral marketing process901B. Adaptiverecombinant system800iincludes multiple instances ofadaptive system100i. Although not shown inFIG. 48, each adaptive system instance, such asadaptive system100i1, may have its own independent set ofprocess participants200, or theprocess participants200 of each adaptive system instance may overlap.

In the embodiment ofFIG. 48, eachadaptive system instance100imanages one or more networks within its structural aspect210 (not shown). These networks may be content networks or process networks, and may be fuzzy networks. As an example, some or all of the structural aspect and/or usage aspect of the firstadaptive system instance100i1 may be syndicated2555 to a secondadaptive system instance100i2, and the structural and/or usage aspects optionally combined. Some or all of the structural and/or usage aspects of the secondadaptive system instance100i2 may then be syndicated2565 to a thirdadaptive system instance100i3, and the structural and/or usage aspects optionally combined. A closed loop may be formed, as some or all of the structural and/or usage aspects of the thirdadaptive system instance100i3 may be syndicated2575 back to the originaladaptive system instance100i1.

Thus, the process of syndication, modification, and combination may continue indefinitely. At each stage, it should be understood that an entireadaptive system instance100imay be syndicated to a recipient that does not have access to theadaptive system instance800i1. And at each stage, the selection, syndication, modification, or combination is enabled by the functions of the adaptiverecombinant system800, as described previously. Thus, the adaptive recommendations function240 of eachadaptive system instance100imay be applied to facilitate these syndications, modifications, and combinations based, in part, on inferences of preferences and interests fromusage behaviors920 ofprocess participants200.

The systems and methods described inFIG. 47 andFIG. 48 may be applied to enabling adaptiveviral marketing process901B, in some embodiments, as depicted inFIGS. 49A and 49B. InFIGS. 49A and 49B, the syndication and recombination of content networks across organization are described. It should be understood that the content networks described may or may not be fuzzy networks, and may or may not be process networks. It should also be understood that the networks may include usage behavioral information associated with the usage aspect220, in addition to, or instead of content networks associated with structural aspect210cof theadaptive system100. Further, although the syndication is to “organizations,” it should be understood that the term as used herein may include a single person.

FIG. 49A depicts a the selection or sub-setting of content network “network1”2735 residing in “organization1”2650 to form “network1a”2695. “Network1a”2695 may contain substantive or non-substantive information (such as advertising or promotional content), and is syndicated to “organization2”2655 for the purposes of either direct promotion, with an option for indirect promotion through re-syndication by “organization2”2655; or the syndication to “organization2”2655 may be for the primary or sole purpose of indirect promotion through “organization2's”2655 expected re-syndication of the network.

In this example, “network1a”2700 and the existing “network2”2705 in “organization2” are combined2710 to form “network2a”2715 in “organization2”2655. This combination may be either for the direct benefit of “organization2”2655, or the purposes of continuing the chain of promotion through re-syndication of a network of substantive and/or non-substantive information that is expected to be increasingly valuable to each new generation of recipients.

Continuing the example, “network2a”2715 is then syndicated to “organization3”2660, wherein “organization3”2660 does not already possess or have access to a content network.

FIG. 49B represents a continuation ofFIG. 49A to depict the potentially closed-loop aspect of the adaptive viral marketing process. “Network2a”2725 in “organization3”2660 is syndicated to “organization1”2655. “Network2a”2725 is then combined with the original “network1”2735 in “organization1”2650 to generate “network3”2740 in “organization1”2650.

FIGS. 49A and 49B demonstrate that, in some embodiments, the adaptiverecombinant process901B may, without limit, enable sub-setting of networks of substantive and/or non-substantive information, syndicating the subsets to one or more destinations, and enabling the syndicated networks to be combined with one or more process networks at the destinations. At each combination step, functions of adaptiverecombinant system800C may be applied, including the relationship resolution functions and the adaptive recommendations function, to create and update process structure (and content) as appropriate. Theparticipants200 in the adaptive viral marketing process may or may not be directly conscious of playing a role in marketing or promotion.

As a specific example of the economics of viral marketing, the originator of the adaptiveviral marketing process901B may supply a product or service for which there are complementary products or services; by complementary, it is meant that the supplier can sell more of its product or services to a customer if the customer has access to, or can purchase, the complementary products or services. So, for example, commentary by other process participants, particularly process participants with special expertise of relevant reputation, may be a complement to selling a tangible or intangible product, such as a video. Through the initiation of the viral marketing approach, delivery or targeted, complementary commentary may be efficiently achieved that could stimulate greater demand for the video itself.

The adaptiveviral marketing process901B ofFIGS. 49A and 49B may also apply methods associated with location-aware collectivelyadaptive system2200 andprocess900H, and may further apply the systems and methods of the adaptive commercial solutions process (900G) depicted inFIG. 43.

Evolvable Processes

According to some embodiments, the adaptiverecombinant process901 may be used to deploy anevolvable process901E across one or more organizations or environments.FIG. 50 depicts an embodiment of the adaptive recombinant computer-basedapplication925R ofFIG. 4C, which includes an evolvable adaptiverecombinant system800e, which itself includes the adaptiverecombinant function850. The adaptiverecombinant function850 in turn includes asyndication function810, a fuzzy network operators function820, and anobject evaluation function830, all of which were described previously. The evolvable adaptiverecombinant system800ealso contains one ormore instances100iof theadaptive system100.Process participants200 generateprocess usage behaviors920 that are tracked and processed by the one or moreadaptive system instances800i. In addition, the evolvable adaptiverecombinant system800econtains anetwork evaluation function860, which is used to evaluate the “fitness” of one or more content networks, which may include process networks, and works inconcert2905 with the adaptiverecombinant function850 to generate new generations of content networks from a previous generation of content networks deemed to be most fit by thenetwork evaluation function860.

Recall fromFIG. 47 that an instance of theadaptive system100 may contain multiple content networks. Thenetwork evaluation function860 may evaluate2915 one or more networks within anadaptive system instance100i3. The adaptiverecombinant function850 may then be applied to create a new generation of recombinant content networks within theadaptive system instance100i3, based on the individual fitness of the previous generation of content networks.

Alternatively, thenetwork evaluation function860 may evaluate2935 content networks acrossadaptive systems instances100i. The adaptiverecombinant function850 may then be applied to create a new generation of recombinant content networks acrossadaptive system instances100i, based on the individual fitness of the previous generation of content networks acrosssystem instances100i.

Thenetwork evaluation function860 may apply criteria derived from inferences on preferences and interests ofusage behaviors920 ofprocess participants200. These criteria may be augmented by additional evaluation criteria and logic as required.

The adaptiverecombinant function850 may generate new generations of content networks based on purely the inheritance of characteristics derived from combinations of previous generations of content networks (Lamarkian approach to network evolution), and/or the adaptiverecombinant function850 may apply random changes to the content networks, so as to create network mutations, which, in turn, increases network variation (Darwinian approach to network evolution). Genetic algorithms may be applied to generate network mutations and combinations.

Evolvable adaptiverecombinant system800ecan therefore enable theevolvable process901E, which can serve as a means of accelerating the development of the most adaptive possible processes for a given organizational environment.

Computing Infrastructure

FIG. 51 depicts various hardware topologies that theadaptive process900, the adaptiverecombinant process901, the adaptive computer-basedapplication925, the adaptive recombinant computer-basedapplication925R, theadaptive system100, or the adaptiverecombinant system800 may embody. Further, the adaptiveasset management process900A, the adaptive real-time learning process900B, theinnovation network process900C, theadaptive publishing process900D, theadaptive commerce process900E, the adaptiveprice discovery process900F, the adaptivecommercial solutions process900G, the location-aware collectivelyadaptive process900H, the recombinantprocess network process901A, the adaptiveviral marketing process901B, theevolvable process901E, or other applications of theadaptive process900 or adaptiverecombinant process901 not described herein may utilize the hardware and computing topologies ofFIG. 51. These various systems are referred to as the “relevant systems,” below.

Servers

950,952, and954 are shown, perhaps residing at different physical locations, and potentially belonging to different organizations or individuals. Astandard PC workstation956 is connected to the server in a contemporary fashion. In this instance, the relevant systems, in part or as a whole, may reside on theserver950, but may be accessed by theworkstation956. A terminal or display-only device958 and aworkstation setup960 are also shown. ThePC workstation956 may be connected to a portable processing device (not shown), such as a mobile telephony device, which may be a mobile phone or a personal digital assistant (PDA). The mobile telephony device or PDA may, in turn, be connected to another wireless device such as a telephone or a GPS receiver.

FIG. 51 also features a network of wireless or otherportable devices962. The relevant systems may reside, in part or as a whole, on all of thedevices962, periodically or continuously communicating with thecentral server952, as required. Aworkstation964 connected in a peer-to-peer fashion with a plurality of other computers is also shown. In this computing topology, the relevant systems, as a whole or in part, may reside on each of thepeer computers964.

Computing system

966 represents a PC or other computing system, which connects through a gateway or other host in order to access theserver952 on which the relevant systems, in part or as a whole, reside. Anappliance968, includes software “hardwired” into a physical device, or may utilize software running on another system that does not itself host the relevant systems. Theappliance968 is able to access a computing system that hosts an instance of one of the relevant systems, such as theserver952, and is able to interact with the instance of the system.

The relevant systems may utilize database management systems, including relational database management systems, to manage to manage associated data and information, including objects and/or relationships among objects. The relevant systems may apply intelligent “swarm” peer-to-peer file sharing techniques to facilitate the syndication of large networks of content, by enabling a plurality of peer computing devices to collectively serve as file servers, thus acting to de-bottleneck the sharing of large networks of information. Further, adaptive recombinant processes may apply intelligent swarm peer-to-peer sharing to the entire network of information (objects and relationships) that is to be syndicated, rather than just individual files. The relevant systems may apply special algorithms to optimally syndicate elements of one or more networks of information across a plurality of peer computing devices to enable the collective set of peer computing devices to be utilized as servers in a manner to enable the most efficient syndication of large-scale networks of information.

While the present invention has been described with respect to a limited number of embodiments, those skilled in the art will appreciate numerous modifications and variations therefrom. It is intended that the appended claims cover all such modifications and variations as fall within the scope of this present invention.